Bishopblog catalogue (updated 12 Mar 2026)

Source: http://www.weblogcartoons.com/2008/11/23/ideas/

Those of you who follow this blog may have noticed a lack of thematic coherence. I write about whatever is exercising my mind at the time, which can range from technical aspects of statistics to the design of bathroom taps. I decided it might be helpful to introduce a bit of order into this chaotic melange, so here is a catalogue of posts by topic.

Language impairment, dyslexia and related disorders

The common childhood disorders that have been left out in the cold (1 Dec 2010) What's in a name? (18 Dec 2010) Neuroprognosis in dyslexia (22 Dec 2010) Where commercial and clinical interests collide: Auditory processing disorder (6 Mar 2011) Auditory processing disorder (30 Mar 2011) Special educational needs: will they be met by the Green paper proposals? (9 Apr 2011) Is poor parenting really to blame for children's school problems? (3 Jun 2011) Early intervention: what's not to like? (1 Sep 2011) Lies, damned lies and spin (15 Oct 2011) A message to the world (31 Oct 2011) Vitamins, genes and language (13 Nov 2011) Neuroscientific interventions for dyslexia: red flags (24 Feb 2012) Phonics screening: sense and sensibility (3 Apr 2012) What Chomsky doesn't get about child language (3 Sept 2012) Data from the phonics screen (1 Oct 2012) Auditory processing disorder: schisms and skirmishes (27 Oct 2012) High-impact journals (Action video games and dyslexia: critique) (10 Mar 2013) Overhyped genetic findings: the case of dyslexia (16 Jun 2013) The arcuate fasciculus and word learning (11 Aug 2013) Changing children's brains (17 Aug 2013) Raising awareness of language learning impairments (26 Sep 2013) Good and bad news on the phonics screen (5 Oct 2013) What is educational neuroscience? (25 Jan 2014) Parent talk and child language (17 Feb 2014) My thoughts on the dyslexia debate (20 Mar 2014) Labels for unexplained language difficulties in children (23 Aug 2014) International reading comparisons: Is England really do so poorly? (14 Sep 2014) Our early assessments of schoolchildren are misleading and damaging (4 May 2015) Opportunity cost: a new red flag for evaluating interventions (30 Aug 2015) The STEP Physical Literacy programme: have we been here before? (2 Jul 2017) Prisons, developmental language disorder, and base rates (3 Nov 2017) Reproducibility and phonics: necessary but not sufficient (27 Nov 2017) Developmental language disorder: the need for a clinically relevant definition (9 Jun 2018) Changing terminology for children's language disorders (23 Feb 2020) Developmental Language Disorder (DLD) in relaton to DSM5 (29 Feb 2020) Why I am not engaging with the Reading Wars (30 Jan 2022)

Autism

Autism diagnosis in cultural context (16 May 2011) Are our ‘gold standard’ autism diagnostic instruments fit for purpose? (30 May 2011) How common is autism? (7 Jun 2011) Autism and hypersystematising parents (21 Jun 2011) An open letter to Baroness Susan Greenfield (4 Aug 2011) Susan Greenfield and autistic spectrum disorder: was she misrepresented? (12 Aug 2011) Psychoanalytic treatment for autism: Interviews with French analysts (23 Jan 2012) The ‘autism epidemic’ and diagnostic substitution (4 Jun 2012) How wishful thinking is damaging Peta's cause (9 June 2014) NeuroPointDX's blood test for Autism Spectrum Disorder ( 12 Jan 2019) Biomarkers to screen for autism (again) (6 Dec 2022)

Developmental disorders/paediatrics

The hidden cost of neglected tropical diseases (25 Nov 2010) The National Children's Study: a view from across the pond (25 Jun 2011) The kids are all right in daycare (14 Sep 2011) Moderate drinking in pregnancy: toxic or benign? (21 Nov 2012) Changing the landscape of psychiatric research (11 May 2014) The sinister side of French psychoanalysis revealed (15 Oct 2019) A desire for clickbait can hinder an academic journal's reputation (4 Oct 2022) Polyunsaturated fatty acids and children's cognition: p-hacking and the canonisation of false facts (4 Sep 2023)

Genetics

Where does the myth of a gene for things like intelligence come from? (9 Sep 2010) Genes for optimism, dyslexia and obesity and other mythical beasts (10 Sep 2010) The X and Y of sex differences (11 May 2011) Review of How Genes Influence Behaviour (5 Jun 2011) Getting genetic effect sizes in perspective (20 Apr 2012) Moderate drinking in pregnancy: toxic or benign? (21 Nov 2012) Genes, brains and lateralisation (22 Dec 2012) Genetic variation and neuroimaging (11 Jan 2013) Have we become slower and dumber? (15 May 2013) Overhyped genetic findings: the case of dyslexia (16 Jun 2013) Incomprehensibility of much neurogenetics research ( 1 Oct 2016) A common misunderstanding of natural selection (8 Jan 2017) Sample selection in genetic studies: impact of restricted range (23 Apr 2017) Pre-registration or replication: the need for new standards in neurogenetic studies (1 Oct 2017) Review of 'Innate' by Kevin Mitchell ( 15 Apr 2019) Why eugenics is wrong (18 Feb 2020)

Neuroscience

Neuroprognosis in dyslexia (22 Dec 2010) Brain scans show that… (11 Jun 2011)  Time for neuroimaging (and PNAS) to clean up its act (5 Mar 2012) Neuronal migration in language learning impairments (2 May 2012) Sharing of MRI datasets (6 May 2012) Genetic variation and neuroimaging (1 Jan 2013) The arcuate fasciculus and word learning (11 Aug 2013) Changing children's brains (17 Aug 2013) What is educational neuroscience? ( 25 Jan 2014) Changing the landscape of psychiatric research (11 May 2014) Incomprehensibility of much neurogenetics research ( 1 Oct 2016)

Reproducibility
Accentuate the negative (26 Oct 2011) Novelty, interest and replicability (19 Jan 2012) High-impact journals: where newsworthiness trumps methodology (10 Mar 2013) Who's afraid of open data? (15 Nov 2015) Blogging as post-publication peer review (21 Mar 2013) Research fraud: More scrutiny by administrators is not the answer (17 Jun 2013) Pressures against cumulative research (9 Jan 2014) Why does so much research go unpublished? (12 Jan 2014) Replication and reputation: Whose career matters? (29 Aug 2014) Open code: note just data and publications (6 Dec 2015) Why researchers need to understand poker ( 26 Jan 2016) Reproducibility crisis in psychology ( 5 Mar 2016) Further benefit of registered reports ( 22 Mar 2016) Would paying by results improve reproducibility? ( 7 May 2016) Serendipitous findings in psychology ( 29 May 2016) Thoughts on the Statcheck project ( 3 Sep 2016) When is a replication not a replication? (16 Dec 2016) Reproducible practices are the future for early career researchers (1 May 2017) Which neuroimaging measures are useful for individual differences research? (28 May 2017) Prospecting for kryptonite: the value of null results (17 Jun 2017) Pre-registration or replication: the need for new standards in neurogenetic studies (1 Oct 2017) Citing the research literature: the distorting lens of memory (17 Oct 2017) Reproducibility and phonics: necessary but not sufficient (27 Nov 2017) Improving reproducibility: the future is with the young (9 Feb 2018) Sowing seeds of doubt: how Gilbert et al's critique of the reproducibility project has played out (27 May 2018) Preprint publication as karaoke ( 26 Jun 2018) Standing on the shoulders of giants, or slithering around on jellyfish: Why reviews need to be systematic ( 20 Jul 2018) Matlab vs open source: costs and benefits to scientists and society ( 20 Aug 2018) Responding to the replication crisis: reflections on Metascience 2019 (15 Sep 2019) Manipulated images: hiding in plain sight (13 May 2020) Frogs or termites: gunshot or cumulative science? ( 6 Jun 2020) Open data: We know what's needed - now let's make it happen (27 Mar 2021) A proposal for data-sharing the discourages p-hacking (29 Jun 2022) Can systematic reviews help clean up science (9 Aug 2022)Polyunsaturated fatty acids and children's cognition: p-hacking and the canonisation of false facts (4 Sep 2023) Book Review: Unreliable (Csaba Szabo) (Mar 16, 2025) Gold standard science isn't gold standard if it's applied selectively - firearms (Aug 26, 2025) Gold standard science isn't gold standard if it's applied selectively - autism (Aug 27, 2025) Wellcome LEAP's new $50M program (Oct 20, 2025) The dangers of using bibliometrics with polluted data (Nov 21, 2025)  

Statistics

Book review: biography of Richard Doll (5 Jun 2010) Book review: the Invisible Gorilla (30 Jun 2010) The difference between p < .05 and a screening test (23 Jul 2010) Three ways to improve cognitive test scores without intervention (14 Aug 2010) A short nerdy post about the use of percentiles (13 Apr 2011) The joys of inventing data (5 Oct 2011) Getting genetic effect sizes in perspective (20 Apr 2012) Causal models of developmental disorders: the perils of correlational data (24 Jun 2012) Data from the phonics screen (1 Oct 2012)Moderate drinking in pregnancy: toxic or benign? (1 Nov 2012) Flaky chocolate and the New England Journal of Medicine (13 Nov 2012) Interpreting unexpected significant results (7 June 2013) Data analysis: Ten tips I wish I'd known earlier (18 Apr 2014) Data sharing: exciting but scary (26 May 2014) Percentages, quasi-statistics and bad arguments (21 July 2014) Why I still use Excel ( 1 Sep 2016) Sample selection in genetic studies: impact of restricted range (23 Apr 2017) Prospecting for kryptonite: the value of null results (17 Jun 2017) Prisons, developmental language disorder, and base rates (3 Nov 2017) How Analysis of Variance Works (20 Nov 2017) ANOVA, t-tests and regression: different ways of showing the same thing (24 Nov 2017) Using simulations to understand the importance of sample size (21 Dec 2017) Using simulations to understand p-values (26 Dec 2017) One big study or two small studies? ( 12 Jul 2018) Time to ditch relative risk in media reports (23 Jan 2020)

Journalism/science communication

Orwellian prize for scientific misrepresentation (1 Jun 2010) Journalists and the 'scientific breakthrough' (13 Jun 2010) Orwellian prize for journalistic misrepresentation: an update (29 Jan 2011) Academic publishing: why isn't psychology like physics? (26 Feb 2011) Scientific communication: the Comment option (25 May 2011)  Publishers, psychological tests and greed (30 Dec 2011) Time for academics to withdraw free labour (7 Jan 2012) 2011 Orwellian Prize for Journalistic Misrepresentation (29 Jan 2012) Communicating science in the age of the internet (13 Jul 2012) How to bury your academic writing (26 Aug 2012) Schizophrenia and child abuse in the media (26 May 2013) Why we need pre-registration (6 Jul 2013) On the need for responsible reporting of research (10 Oct 2013) Psychology research: hopeless case or pioneering field? (28 Aug 2015) When scientific communication is a one-way street (13 Dec 2016) Time to ditch relative risk in media reports (23 Jan 2020) Book Review. Fiona Fox: Beyond the Hype (12 Apr 2022)

Academic Publishing

Science journal editors: a taxonomy (28 Sep 2010) Time for neuroimaging (and PNAS) to clean up its act (5 Mar 2012) High-impact journals: where newsworthiness trumps methodology (10 Mar 2013)  A short rant about numbered journal references (5 Apr 2013) Desperate marketing from J. Neuroscience ( 18 Feb 2016) Editorial integrity: publishers on the front line ( 11 Jun 2016) A New Year's letter to academic publishers (4 Jan 2014) Journals without editors: What is going on? (1 Feb 2015) Editors behaving badly? (24 Feb 2015) Will Elsevier say sorry? (21 Mar 2015) How long does a scientific paper need to be? (20 Apr 2015) Will traditional science journals disappear? (17 May 2015) My collapse of confidence in Frontiers journals (7 Jun 2015) Publishing replication failures (11 Jul 2015) Breaking the ice with buxom grapefruits: Pratiques de publication and predatory publishing (25 Jul 2017) Should editors edit reviewers? ( 26 Aug 2018) Corrigendum: a word you may hope never to encounter (3 Aug 2019) Percent by most prolific author score and editorial bias (12 Jul 2020) PEPIOPs – prolific editors who publish in their own publications (16 Aug 2020) Faux peer-reviewed journals: a threat to research integrity (6 Dec 2020) Time for publishers to consider the rights of readers as well as authors (13 Mar 2021) Universities vs Elsevier: who has the upper hand? (14 Nov 2021) We need to talk about editors (6 Sep 2022) So do we need editors? (11 Sep 2022) Reviewer-finding algorithms: the dangers for peer review (30 Sep 2022) A desire for clickbait can hinder an academic journal's reputation (4 Oct 2022) What is going on in Hindawi special issues? (12 Oct 2022) New Year's Eve Quiz: Dodgy journals special (31 Dec 2022) A suggestion for e-Life (20 Mar 2023) Papers affected by misconduct: Erratum, correction or retraction? (11 Apr 2023) Is Hindawi “well-positioned for revitalization?” (23 Jul 2023) The discussion section: Kill it or reform it? (14 Aug 2023) Spitting out the AI Gobbledegook sandwich: a suggestion for publishers (2 Oct 2023) The world of Poor Things at MDPI journals (Feb 9 2024) Some thoughts on eLife's New Model: One year on (Mar 27 2024) Does Elsevier's negligence pose a risk to public health? (Jun 20 2024) Collapse of scientific standards at MDPI journals: a case study (Jul 23 2024) My experience as a reviewer for MDPI (Aug 8 2024) Optimizing research integrity investigations: the need for evidence (Aug 22 2024) Now you see it, now you don't: the strange world of disappearing Special Issues at MDPI (Sep 4 2024) Prodding the behemoth with a stick (Sep 14 2024) Using PubPeer to screen editors (Sep 24 2024) An open letter regarding Scientific Reports (Oct 16 2024) What's going on at the Journal of Psycholinguistic Research? (Oct 21, 2024) Finland vs Germany: the case of MDPI (Dec 23, 2024) Tomatoes roaming the fields: another embarrassing paper for MDPI (Jan 18, 2025) IEEE has a pseudoscience problem (Feb 22, 2025) Trouble at t'(review) mill: How MDPI lets down authors (July 21 ,2025) New publishing models will only work if authors embrace them (July 31, 2025) Problems with eLife's new article type: Replication studies (Oct 27, 2025) The inner workings of a paper mill (Nov 8, 2025) An open letter to the BMJ editorial board (Jan 5, 2026) An analysis of PubPeer comments on highly-cited retracted articles (Feb 2, 2026) Stealth corrections are still a threat to academic integrity (Feb 20, 2026) 

Social Media

A gentle introduction to Twitter for the apprehensive academic (14 Jun 2011) Your Twitter Profile: The Importance of Not Being Earnest (19 Nov 2011) Will I still be tweeting in 2013? (2 Jan 2012) Blogging in the service of science (10 Mar 2012) Blogging as post-publication peer review (21 Mar 2013) The impact of blogging on reputation ( 27 Dec 2013) WeSpeechies: A meeting point on Twitter (12 Apr 2014) Email overload ( 12 Apr 2016) How to survive on Twitter - a simple rule to reduce stress (13 May 2018)

Academic life

An exciting day in the life of a scientist (24 Jun 2010) How our current reward structures have distorted and damaged science (6 Aug 2010) The challenge for science: speech by Colin Blakemore (14 Oct 2010) When ethics regulations have unethical consequences (14 Dec 2010) A day working from home (23 Dec 2010) Should we ration research grant applications? (8 Jan 2011) The one hour lecture (11 Mar 2011) The expansion of research regulators (20 Mar 2011) Should we ever fight lies with lies? (19 Jun 2011) How to survive in psychological research (13 Jul 2011) So you want to be a research assistant? (25 Aug 2011) NHS research ethics procedures: a modern-day Circumlocution Office (18 Dec 2011) The REF: a monster that sucks time and money from academic institutions (20 Mar 2012) The ultimate email auto-response (12 Apr 2012) Well, this should be easy…. (21 May 2012) Journal impact factors and REF2014 (19 Jan 2013)  An alternative to REF2014 (26 Jan 2013) Postgraduate education: time for a rethink (9 Feb 2013)  Ten things that can sink a grant proposal (19 Mar 2013)Blogging as post-publication peer review (21 Mar 2013) The academic backlog (9 May 2013)  Discussion meeting vs conference: in praise of slower science (21 Jun 2013) Why we need pre-registration (6 Jul 2013) Evaluate, evaluate, evaluate (12 Sep 2013) High time to revise the PhD thesis format (9 Oct 2013) The Matthew effect and REF2014 (15 Oct 2013) The University as big business: the case of King's College London (18 June 2014) Should vice-chancellors earn more than the prime minister? (12 July 2014)  Some thoughts on use of metrics in university research assessment (12 Oct 2014) Tuition fees must be high on the agenda before the next election (22 Oct 2014) Blaming universities for our nation's woes (24 Oct 2014) Staff satisfaction is as important as student satisfaction (13 Nov 2014) Metricophobia among academics (28 Nov 2014) Why evaluating scientists by grant income is stupid (8 Dec 2014) Dividing up the pie in relation to REF2014 (18 Dec 2014)  Shaky foundations of the TEF (7 Dec 2015) A lamentable performance by Jo Johnson (12 Dec 2015) More misrepresentation in the Green Paper (17 Dec 2015) The Green Paper’s level playing field risks becoming a morass (24 Dec 2015) NSS and teaching excellence: wrong measure, wrongly analysed (4 Jan 2016) Lack of clarity of purpose in REF and TEF ( 2 Mar 2016) Who wants the TEF? ( 24 May 2016) Cost benefit analysis of the TEF ( 17 Jul 2016)  Alternative providers and alternative medicine ( 6 Aug 2016) We know what's best for you: politicians vs. experts (17 Feb 2017) Advice for early career researchers re job applications: Work 'in preparation' (5 Mar 2017) Should research funding be allocated at random? (7 Apr 2018) Power, responsibility and role models in academia (3 May 2018) My response to the EPA's 'Strengthening Transparency in Regulatory Science' (9 May 2018) More haste less speed in calls for grant proposals ( 11 Aug 2018) Has the Society for Neuroscience lost its way? ( 24 Oct 2018) The Paper-in-a-Day Approach ( 9 Feb 2019) Benchmarking in the TEF: Something doesn't add up ( 3 Mar 2019) The Do It Yourself conference ( 26 May 2019) A call for funders to ban institutions that use grant capture targets (20 Jul 2019) Research funders need to embrace slow science (1 Jan 2020) Should I stay or should I go: When debate with opponents should be avoided (12 Jan 2020) Stemming the flood of illegal external examiners (9 Feb 2020) What can scientists do in an emergency shutdown? (11 Mar 2020) Stepping back a level: Stress management for academics in the pandemic (2 May 2020)
TEF in the time of pandemic (27 Jul 2020) University staff cuts under the cover of a pandemic: the cases of Liverpool and Leicester (3 Mar 2021) Some quick thoughts on academic boycotts of Russia (6 Mar 2022) When there are no consequences for misconduct (16 Dec 2022) Open letter to CNRS (30 Mar 2023) When privacy rules protect fraudsters (Oct 12, 2023) Defence against the dark arts: a proposal for a new MSc course (Nov 19, 2023) An (intellectually?) enriching opportunity for affiliation (Feb 2 2024) Just make it stop! When will we say that further research isn't needed? (Mar 24 2024) Are commitments to open data policies worth the paper they are written on? (May 26 2024) Whistleblowing, research misconduct, and mental health (Jul 1 2024) I don't care about journal impact factors but I do care about visibility (Oct 27, 2024) Why I have resigned from the Royal Society (Nov 25, 2024) Seven reasons for keeping Elon Musk as a Fellow of the Royal Society (Feb 12, 2025) The dangers of using bibliometrics with polluted data (Nov 21, 2025)

Celebrity scientists/quackery

Three ways to improve cognitive test scores without intervention (14 Aug 2010) What does it take to become a Fellow of the RSM? (24 Jul 2011) An open letter to Baroness Susan Greenfield (4 Aug 2011) Susan Greenfield and autistic spectrum disorder: was she misrepresented? (12 Aug 2011) How to become a celebrity scientific expert (12 Sep 2011) The kids are all right in daycare (14 Sep 2011)  The weird world of US ethics regulation (25 Nov 2011) Pioneering treatment or quackery? How to decide (4 Dec 2011) Psychoanalytic treatment for autism: Interviews with French analysts (23 Jan 2012) Neuroscientific interventions for dyslexia: red flags (24 Feb 2012) Why most scientists don't take Susan Greenfield seriously (26 Sept 2014) NeuroPointDX's blood test for Autism Spectrum Disorder ( 12 Jan 2019) Low-level lasers. Part 1. Shining a light on an unconventional treatment for autism (Nov 25, 2023) Low-level lasers. Part 2. Erchonia and the universal panacea (Dec 5, 2023)

Women

Academic mobbing in cyberspace (30 May 2010) What works for women: some useful links (12 Jan 2011) The burqua ban: what's a liberal response (21 Apr 2011) C'mon sisters! Speak out! (28 Mar 2012) Psychology: where are all the men? (5 Nov 2012) Should Rennard be reinstated? (1 June 2014) How the media spun the Tim Hunt story (24 Jun 2015)

Politics and Religion

Lies, damned lies and spin (15 Oct 2011) A letter to Nick Clegg from an ex liberal democrat (11 Mar 2012) BBC's 'extensive coverage' of the NHS bill (9 Apr 2012) Schoolgirls' health put at risk by Catholic view on vaccination (30 Jun 2012) A letter to Boris Johnson (30 Nov 2013) How the government spins a crisis (floods) (1 Jan 2014) The alt-right guide to fielding conference questions (18 Feb 2017) We know what's best for you: politicians vs. experts (17 Feb 2017) Barely a good word for Donald Trump in Houses of Parliament (23 Feb 2017) Do you really want another referendum? Be careful what you wish for (12 Jan 2018) My response to the EPA's 'Strengthening Transparency in Regulatory Science' (9 May 2018) What is driving Theresa May? ( 27 Mar 2019) A day out at 10 Downing St (10 Aug 2019) Voting in the EU referendum: Ignorance, deceit and folly ( 8 Sep 2019) Harry Potter and the Beast of Brexit (20 Oct 2019) Attempting to communicate with the BBC (8 May 2020) Boris bingo: strategies for (not) answering questions (29 May 2020) Linking responsibility for climate refugees to emissions (23 Nov 2021) Response to Philip Ball's critique of scientific advisors (16 Jan 2022) Boris Johnson leads the world ....in the number of false facts he can squeeze into a session of PMQs (20 Jan 2022) Some quick thoughts on academic boycotts of Russia (6 Mar 2022) Contagion of the political system (3 Apr 2022)When there are no consequences for misconduct (16 Dec 2022)

Humour and miscellaneous Orwellian prize for scientific misrepresentation (1 Jun 2010) An exciting day in the life of a scientist (24 Jun 2010) Science journal editors: a taxonomy (28 Sep 2010) Parasites, pangolins and peer review (26 Nov 2010) A day working from home (23 Dec 2010) The one hour lecture (11 Mar 2011) The expansion of research regulators (20 Mar 2011) Scientific communication: the Comment option (25 May 2011) How to survive in psychological research (13 Jul 2011) Your Twitter Profile: The Importance of Not Being Earnest (19 Nov 2011) 2011 Orwellian Prize for Journalistic Misrepresentation (29 Jan 2012) The ultimate email auto-response (12 Apr 2012) Well, this should be easy…. (21 May 2012) The bewildering bathroom challenge (19 Jul 2012) Are Starbucks hiding their profits on the planet Vulcan? (15 Nov 2012) Forget the Tower of Hanoi (11 Apr 2013) How do you communicate with a communications company? ( 30 Mar 2014) Noah: A film review from 32,000 ft (28 July 2014) The rationalist spa (11 Sep 2015) Talking about tax: weasel words ( 19 Apr 2016) Controversial statues: remove or revise? (22 Dec 2016) The alt-right guide to fielding conference questions (18 Feb 2017) My most popular posts of 2016 (2 Jan 2017) An index of neighbourhood advantage from English postcode data ( 15 Sep 2018) Working memories: A brief review of Alan Baddeley's memoir ( 13 Oct 2018) New Year's Eve Quiz: Dodgy journals special (31 Dec 2022) Retrospective look at blog highlights of 2024 (Jan 1, 2025)

A LEAP into the future, or off a cliff: Wellcome LEAP's new $50M program

A few days ago, I saw this post on LinkedIn:

How does the gut microbiome shape early brain development? That’s what FORM, a new $50 million programme from Wellcome Leap, aims to answer. Critically, it wants to identify the role of the microbiome in autism and other neurological disorders. Applicants from universities, companies and non-profits are invited to submit project proposals by 14 November. 

The full programme announcement for FORM (Foundations of a Resilient Microbiome) that you can download here hops around citing various references that indicate the microbiome is important for early development and can be influenced by factors such as antibiotics. So far, so uncontroversial. But then the topic of autism is introduced.

First we hear that autism diagnoses have increased. Then, a section devoid of references states:

 Many have attributed this increase to expanded surveillance, broadening of diagnostic categories (to include milder autism-related difficulties), or increased public awareness. While all are true, the significance of the increase suggests other rising risk factors may also be contributing. 

We then are told that this can't be due to genes because they are pretty stable in populations, and so we seem led remorselessly to the conclusion that it must be an environmental factor, and what better culprit could there be than the microbiome.

If I was going to predicate a $50 million research program on that premise, I'd do a bit more research into those studies on the increase in diagnosis. Diagnostic criteria have changed radically, so children who would have in the past had other diagnoses, or no diagnosis, are now encompassed within autism. Furthermore, there is wider understanding of autism, and a diagnosis can bring with it educational support, which can be a reason why parents will seek a diagnosis. Here's a simple explainer that I wrote in 2012, and subsequent studies by Lundström et al (2015), Cardinal et al (2016) and Zeidan et al (2022).

The fragments of supportive evidence that are provided for the autism/microbiome link seem cherrypicked and are not impressive. At least one claim seems just plain wrong: "Babies exposed to antibiotics in the first 6 months of life may be twice as likely to develop ASD as those exposed later." The cited paper by Azad et al (2016) doesn't mention autism and I when I searched for another source, I found a solid-looking study claiming no association. Other cited results are the kinds of findings that you get if you test for so many associations that some are bound to come up by chance. The handful of animal model studies that are cited have been criticised on methodological grounds.

Things go more seriously off the rails when specific quantitative goals are set for the program: 

Autism currently affects about 3.2% of children. To identify what proportion of these cases may be attributable to gut microbiome dysfunction, we will need objective biomarkers that can detect the dysfunction with high accuracy (balanced accuracy >90%). Establishing this will require a large cohort — likely more than 15,000 children — to ensure statistical power. With that sample size, we can reliably estimate whether microbiome dysfunction accounts for as much as 50% of ASD cases (around 1.6% of all children) or as little as 10% (about 0.3%).

Three things about this:

1. In a footnote it is noted that "Severe autism with an established genetic origin (about 10–20%) and mild/ moderate ASD (~40%) fall outside the scope of this program" - these estimates don't seem to take that into account. And it's not clear if the databases that will be used for the analysis actually allow one to distinguish autism subtypes.
2. You can't establish causality from observational data. As has been shown by Yap et al (2021), the microbiome is influenced by specific dietary preferences of autistic children.
3. It is assumed that the lower bound is that microbiome dysfunction explains 10% of cases. This shows remarkable commitment to a causal hypothesis that has no solid evidence: a realistic lower bound would be zero.

As regards the plans in "Thrust 2" to have a diagnostic set of biomarkers that will predict severe autism-related difficulties, there are so many issues here, that I recommend reading previous blogposts I wrote on this topic, here, and here.  In brief, screening is only effective if there is a strong association between biomarkers and outcomes and if the biomarker measures are stable. Even if those conditions are met, if the base rate of the condition (autism) is low, you will be overwhelmed with false positives.

I was surprised that a reputable funding body was associated with this program, so I wanted to find out more about Wellcome LEAP.  They are a U.S.-based non-profit organization founded by the Wellcome Trust that:

builds bold, unconventional programs, and funds them at scale. Programs optimized to deliver breakthroughs in human health over 5 – 10 years and demonstrate seemingly impossible results on seemingly impossible timelines.

No doubt I'm too conventional, but I'm nervous of "seemingly impossible" things. If they are claimed, I am suspicious, especially if this occurs on "seemingly impossible timelines".

Reading on, I can see lots of things to like about LEAP. The idea is to cut time spent in bureaucratic processes of setting up grants, and to bring together networks of researchers from different institutions and different disciplines who can work together to solve problems that involve large and complex datasets, and check generalisability of findings. This kind of international collaborative approach that involves diverse populations is a definite bonus of LEAP.

The worrying bit was the emphasis on speed - especially since this was coupled with an expectation that the results would be commercialised.

This is clarified here:

Wellcome Leap anticipates that it will normally further our mission (and the organization’s mission) to commercialize the results of Wellcome Leap-funded research. If we determine that a Performer’s organization is not making appropriate efforts to further commercialization, either itself or through a third party (e.g., a licensee), we have the option to request a meeting and a remedial commercialization plan to address the issue.

Given that I have in the past written in favour of slow science, it's perhaps not surprising that this funding model doesn't appeal to me. The thing is that not all delays in scientific progress are down to bureaucracy or timidity. A major obstacle to progress is time wasted trying to build on prior research findings that prove to be illusory.

Science should be cumulative, which means we should be able to proceed with confidence and assume that published research is robust. The likelihood of this being the case is low if we are dealing with complex multidimensional systems, where the temptation is to just hunt around until we find something that looks exciting, embellish it with a few statistical credentials and claim we have a novel result.  As Chin (2025) has argued, when put under pressure to deliver speedy results, scientists may be forced into a position of hyping their findings, cutting corners, and reporting only favourable results. 

I was frankly dismayed to read that in her prior role as Program Director for the Wellcome Leap “First 1000 Days” (1kD) initiative, the Program Director of FORM:

led the delivery of multiple new product opportunities to improve cognitive development in the first 1000 days of a child’s life — including a breakthrough microbiome-directed diagnostic and therapeutic now positioned for commercialization

The 1kD initiative was funded in summer of 2021. So it seems that in four years a microbiome-directed "diagnostic and therapeutic" has been developed and validated. I'm afraid that without solid published evidence, this looks like NeuroPointDX all over again. 

Wellcome LEAP programs are focused around "What If" questions. My question is "What if there is no association between autism and microbiome dysfunction?" All three "thrusts" of this program depend on there being an association. My suggestion is to set aside 1% of the funds for this program for pre-registered replications of the studies that are cited as foundational for the research. I know the idea is to fund high-risk, unconventional research, but a lot of time and money could be saved by checking that the foundations are solid before building an edifice on this premise. 

 Comments on this blog are moderated so may take time to appear.  In general, anonymous comments are not approved. 

Gold standard science isn't gold standard if it's applied selectively. Part 2: Research on causes of autism

In my previous blogpost, I discussed why many scientists can't take seriously the lofty ideals expressed in Trump's plans for Gold Standard Science, even though the basic principles seem excellent. Given the current Republican administration's catastrophic track record in undermining US science, their demands for high standards ring hollow. It appears that demands for Gold Standard Science will be weaponised against types of science they don't like. 

Today I provide evidence from the opposite side of the fence. If the Trump administration was really serious about Gold Standard Science, then they should lead by example and showcase projects that are impeccably rigorous, open and transparent. 

And what better place to start than with research on autism, which has been an obsession of Robert F Kennedy Jr (RFK), the 26th United States Secretary of Health and Human Services? The first inkling of what was to come was in April this year when the media reported that he had planned to find the cause of autism by September.  Then, on May 27th, NIH announced a new Autism Data Science Initiative, which appears to relate to JFK's plans. 

I watched with interest a video that was released on 10th June explaining the background of the project. The NIH staff presenting the video gave an evidence-based and balanced account of what is known about the etiology of autism, which was described as complex and multifactorial, involving different types of genetic causes which may interact with environmental factors. In considering the increase in autism diagnosis over time, it was noted that changes in diagnostic criteria, and the need for a diagnosis for access to services were implicated. They implicitly accepted, however, the notion that these factors were insufficient to explain the increase, and so we needed research on other factors. 

They then explained how to apply for a share of the $50 million allocated to the initiative, which seemed designed to encourage machine learning approaches to data mining of autism-relevant datasets "to explore the contribution of genetic and non-genetic factors to the causes of autism and or to identify patterns associated with intervention outcomes and the use of services for autism." 

A startling feature was that submissions had to be in by 27th June, one month after the scheme was announced, and 17 days after the instructional video was posted. The earliest start date was 1st September, perhaps prompted by RFK's idea of having autism etiology done and dusted by then. 

You might wonder how this works with NIH's review process: the answer is that it doesn't. The funding mechanism is an Other Transaction: "an assistance mechanism that is not a grant, contract, or cooperative agreement" and the proposal does not undergo traditional NIH review, but instead is subject to "an objective scientific review. This ensures the assessment of scientific or technical merit of applications by individuals with knowledge and expertise equivalent to that of the individuals whose applications of support they are reviewing". Though, so far, it seems that the top US autism researchers have not been consulted on this scheme. Furthermore, the phrase "Autism Data Science Initiative" isn't mentioned in the massive report from the Committee on Appropriations that was published on July 31st. So this has a decidedly ad hoc feel to it. 

This hurried process does not seem an optimal way to foster Gold Standard Science, which requires thought and care to go into research plans. There seem two possible explanations for this rushed approach. Either those who devised the scheme are so ignorant that they don't understand how long it takes to develop a strong research proposal, or they really don't want anyone to apply to the scheme other than specific cronies who will do their bidding. 

I had thought we might have to wait until the end of September when the successful grants are announced to see who the lucky grant recipients would be. From this video clip of a recent Cabinet meeting, however, it seems that the research has already been done, results are in and will be announced next month! 

Donald Trump and Jay Bhattacharya can talk about Gold Standard Science as much as they like: scientists can see for themselves this travesty of research process, which indicates that, when it comes to their own studies, those in power will know the answer before the data is in. No transparency, no pre-registration, no open data and code, no communication of error and uncertainty, no skepticism of findings or attempt to falsify hypotheses, and no impartial peer review. Truly this is Tinsel Standard Science.

IEEE Has a Pseudoscience Problem

Guest post by Solal Pirelli

The IEEE, full name Institute of Electrical and Electronics Engineers, is one of the main scientific publishers in domains related to its name. Many IEEE venues, such as ICSE in software engineering and IROS in robotics, are “top” venues that publish important research. While these are conferences and not journals, computer science and related fields are unusual in that conferences are typically the more prestigious option.

But as I’ve covered before in the case of another big computer science publisher, world-class research can coexist with world-class nonsense. Many not-so-top IEEE venues publish “AI gobbledegook sandwiches”, pointless papers that apply standard machine learning or artificial intelligence to basic data sets resulting in vague predictions supposedly improving on ill-defined baselines.

Unfortunately, bad science published by IEEE isn’t limited to boring applications of boring algorithms to boring data. In this blog post, I’ll present IEEE-published pseudoscience of various kinds, show how this correlates with other problems, and discuss why publishers don’t do enough about it. 

All kinds of quackery 

The IEEE has published numerous new “methods” to help providers or users of pseudoscientific disciplines. Ayurveda is enhanced with a “preprocessing framework” to detect diabetes, a neural network to classify herbs, and even an AI assistant. Astrology is automated with a machine learning model. Myers-Briggs personality type testing is granted another neural network.

Some IEEE papers are at the very fringe of pseudoscience, unconventional even by quack standards. A symposium on antennas and propagation published three papers by the same author on “scientific traditional Chinese medicine”, a variant based on electromagnetism and 5G with “supernatural potential” (see here, here and here). An Indian conference on electronics published four papers (here, here, here and here) by the same first author on “electro-homeopathy”, the brainchild of a 19th century Italian count that an Indian high court called “nothing but quackery” a decade before these papers were published.

Of course, no list of pseudoscience would be complete without perpetual motion. That’s right, the IEEE has published two papers on perpetual motion in 2017 and 2022! How these were not desk-rejected is anyone’s guess.

Even work that is not pseudoscientific in itself can propagate harmful or downright absurd stereotypes. Consider what IEEE-published and supposedly peer-reviewed papers have to say about autism: 

• “Children with autism require constant care because you never know what will trigger them” (source) 
• "If symptoms of autism are detected early, children with autism usually return to normal development after effective medical intervention" (source) 
• “A baby born with autism spectrum disorder may have a lower-than-average heart rate. Complete blockage of the heart at birth is rare. Abnormal heart rate leads to heart block. So, there is a high chance of the child's death due to permanent heart blockage at any time.” (source)

Why it matters

One may think that such papers won’t cause harm because they’re unlikely to be read, since they are mostly in unknown venues and unrelated to the IEEE’s domain. While I personally disagree since I believe publishing pseudoscience risks breaking the public’s trust in legitimate research, let me provide a more objective argument. Pseudoscience in papers is heavily correlated with other problematic practices that are more difficult to detect automatically. This makes searching for pseudoscience an effective way to find problematic venues, complementary to existing techniques.

The preprocessing framework to detect diabetes with Ayurveda? In a conference that accepts papers on the same day they are submitted, somehow speeding up the weeks or months usually necessary for proper peer review.

The neural network that classifies Ayurvedic herbs? In a conference that plagiarized its peer review policy from Elsevier’s “Transport Policy” journal. Look for fragments of this policy in your favorite search engine and you’ll find a surprising number of venues that have done so, seemingly without noticing the references to Transport Policy.

The four papers on electro-homeopathy? In a conference that published a mathematical “algorithm” amounting to high school mathematics. While exact definitions of “novelty” vary, no one could credibly claim that this paper is novel enough for a scientific conference.

The 2017 paper on perpetual motion? In a conference that didn’t notice an entirely plagiarized section in that paper, ironically from a source explaining why perpetual motion is impossible. How this is compatible with IEEE’s policy of checking all content for plagiarism is unclear.

The paper claiming “you never know what will trigger” autistic children? In a conference supposedly happening in a London office building, whose four IEEE-published editions only feature one paper from a European university among a sea of India-based authors. Did the authors of this conference’s papers really travel to the other side of the globe to present in a place not designed for presentations?

The neural network for Myers-Briggs? In a conference chaired by a professor whose Russian university is under sanctions from the US, the EU, Ukraine, and even Switzerland!

Action is rare 

The expected process here would be to report this nonsense to the publisher, who would investigate, quickly conclude these papers should never have been published, lose faith in the peer review process that led to their acceptance, and issue retractions. Barring extremely strong evidence from conference chairs that some cases were truly one-off exceptions, such retractions would cover entire editions of conferences.

This happens… sometimes. The IEEE has retracted papers before, such as this one after “only” five months. They have also retracted entire venues, such as this one totaling 400 papers, four years after it was reported.

 
But the IEEE frequently does not react at all to reports. Guillaume Cabanac, who specializes in scientific fraud detection, has repeatedly and publicly called them out. For instance, he’s reported telltale signs of ChatGPT as in this paper that includes “Regenerate Response” in the middle of text and this paper that includes “I am unable to […] due to the fact I am an AI language model”. He’s also reported “tortured phrases”, attempts at avoiding plagiarism detection that instead create nonsense such as “parcel misfortune” instead of “packet loss” in computer networking, in sometimes large concentrations. Cabanac and other sleuths have published “proceedings-level reports” on PubPeer, such as this one, when entire IEEE conferences have problems. None of the examples in this paragraph have led to any public reaction from the IEEE.

The IEEE occasionally issues “expressions of concern”, such one as for this paper over a year after concrete evidence of plagiarism was publicly reported. But expressions of concerns are not retractions. In mid-2023, Retraction Watch noted that hundreds of IEEE papers reported by Guillaume Cabanac and Harvard lecturer Kendra Albert were still up for sale. A year and a half later, that remains the case.

One case noted above is particularly noteworthy in terms of both reputation and IEEE awareness: The “scientific TCM” papers were published in the 2022 and 2023 editions of the “International Symposium on Antennas and Propagation”, a 6-decade-old conference whose 2024 edition boasted the IEEE President as a keynote speaker. Clearly, the IEEE is aware of the venue and its papers. What’s the point in “reporting” them?

Processes are inadequate 

The scale of publishers’ actions is nowhere near the scale of the problem. Creating a new conference or journal does not require that much time if the peer reviewing process is fake. As long as the average time it takes a publisher to retract a venue is higher than the time it takes to create a new venue, there won’t be meaningful progress.

Current publisher processes are designed to correct honest mistakes, not to fight malice. The time it takes to contact authors, wait for their response, wait for them to find original data, and so on is worth it when a single paper has a problem that can be explained by human error. But any such process is a waste of time when a paper contains blatant pseudoscience, has obviously been plagiarized, or uses terminology so bizarre no reviewer could have understood it.

To give an example of scale, here’s a collision of pseudoscience and tortured phrases. The paper on an AI assistant for ayurveda mentioned earlier is in the “2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT)”. Guillaume Cabanac’s Problematic Paper Screener currently lists 185 cases of tortured phrases manually confirmed by Cabanac himself, with another 160 pending assessment. These include “herbal language” instead of natural language, “system getting to know” instead of machine learning, “give-up-to-give-up” instead of end-to-end, and “0.33-celebration” instead of third-party.  

Individually contacting and waiting for hundreds of authors just in case they can explain why their paper talks about 0.33-celebrations isn’t going to cut it. Neither is individually contacting and waiting for dozens of conference editors just in case they can explain why their peer review process didn’t spot this nonsense. 

What can we do?

Given the incentives and processes at play, it’s not surprising to see the IEEE or any other big publisher publish pseudoscience. The authors of the papers mentioned in this post probably didn’t do anything illegal, except maybe for occasional plagiarism of copyrighted content, but nobody has the time and money to sue for such boring violations. This gives publishers a double excuse: they’re not publishing anything illegal, and retractions without a solid legal basis could backfire.

The scientific community needs to ban the “incompetence” defense from authors and stop associating with publishers that can’t be bothered to act quickly enough.  

Authors who publish obvious nonsense should not get a chance to explain themselves or “correct” their paper. 

Publishers make enough money from processing and selling articles. They can defend themselves from occasional lawsuits by angry authors, and they can hire scientific integrity specialists.

When I say “scientific integrity specialist”, that can unfortunately be as simple as “person looking for specific keywords in Google Scholar”. It’s what I did to find pseudoscience, and you can do that too. Report these on PubPeer, directly to publishers, or both. You can also go to the Problematic Paper Screener’s page listing articles that have not been manually assessed yet, and follow the instructions.

Finally, remember that most scientists have no idea this is going on. You can help by publicly calling out problematic papers and lack of action. Ask candidates for governance boards in more democratic publishers like the IEEE what they plan to do about fraud. Discourage institutions, especially public ones in democratic countries, from making blanket deals with publishers.

Collapse of scientific standards at MDPI journals: a case study

 

"Here's one for you", said my husband, as he browsed the online Daily Telegraph*: "Severe autism can be reversed, groundbreaking study suggests".  My heart did not lift up at this news, which was also covered in the Daily Mail; it is a near certainty that any study making such claims is flawed. But I found it hard to believe just how flawed it turned out to be.  

 

The article, by D'Adamo et al.,  was published in a special issue of an MDPI journal, the Journal of Personalized Medicine.  The special issue "A Personalized Medicine Approach to the Diagnosis and Management of Autism Spectrum Disorder: Beyond Genetic Syndromes" appears largely to be a vehicle for papers by the guest editor Richard E. Frye, who co-authored 3/4 editorials, 3/9 articles and 1/1 review in this collection.  He was editor but not an author on the paper by D'Adamo et al, which is categorised as a "case report".

 

Essentially, this is a description of development of a pair of non-identical twin girls who were diagnosed with autism at 20 months of age, along with various gastric and motor conditions, and were subsequently subjected to a remarkable list of interventions, viz:

·      Parents worked with an autism parent coach who informed them about the link between "total allostatic load" and developmental disorders, a concept promoted by the Documenting Hope Project that one of the authors is affiliated with

·      Parents accessed resources, including free webinars, through Epidemic Answers

·      Parents took part in a parent forum called "Healing Together"

·      Applied Behavior Analysis (ABA) from 22 mo to 33 mo

·      Speech Therapy starting at 24 mo

·      Rigorous diet and nutrition intervention eliminating sources of glutamate, following Reduced Excitatory Inflammatory Diet

·      A strict gluten-free, casein-free diet that was low in sugar and additives

·      Dietary supplements, including omega-3 fatty acids, multivitamins, vitamin D, carnitine, 5-methyltetrahydrofolate and "bio-individualized homeopathic remedies"

·      Family consulted a naturopathic doctor who used IntellxxDNA genomics tool to recommend diet rich in tryptophan, vitamins B12, B6 and folate, betaine and choline, lion's mane mushroom and resolvins, as well as some dietary variants specific to each twin.

·      Neuro-sensory motor reflex integration given by an occupational therapist

·      Environmental evaluation of the home for air quality, mould and moisture, culminating in a visit by a Building Biology Environmental Consultant who identified possible water damage.

·      Cranial osteopathy, as recommended by a developmental optometrist.

 

When first diagnosed, the girls were given an assessment called the Autism Treatment Evaluation Checklist, which was repeated after 18 months. Scores declined (i.e. improved) for both girls, with one twin improving substantially, and the other less so.

 

I haven't been able to find any norms for the ATEC, but there's a bit of data on trajectories in a study by Mahapatra et al (2018) and this shows that scores normally improve between the age of 2 and 3 years.  We may also note that the ATEC is completed by a parent, and so may be influenced by parental expectations of improvement.

 

The authors conclude the study demonstrates "the clear environmental and lifestyle influences on ASD" and that there is "comparatively greater impact of these types of factors than genetics".  Neither conclusion is warranted. We can't know whether these changes would have occurred in the absence of the numerous interventions that the children were given.  If the interventions did have an effect, it would be impossible to tell which were the key ingredients, because all were given together.

 

The authors grudgingly state "...while effective at reversing ASD diagnoses, the comprehensive approach that was employed in this case may not yet be widely generalizable".  But they didn't show their approach was effective - at least not by any conventional standards of evidence-based intervention. 

 

Another limitation they noted was that this was an expensive regimen that might be out of reach for parents with limited resources.

 

Should the journalists who gave this study coverage be criticised? I think the answer is yes: science journalists can't be expected to be experts in all areas of science, but they should be aware of some basic principles, such as the need for adequate sample sizes and control groups in intervention studies, and the need to be careful in assuming causality. They can, if uncertain, ask the Science Media Centre to put them in touch with knowledgeable experts who could advise. This is particularly important for a sensitive subject matter such as autism, where publicity for unfounded interventions that portray autism as a "disease" requiring "healing" has potential to do harm.  At minimum, they might have noted the ethics statement that with only two children involved, "this is not considered a systematic investigation designed to contribute to generalizable knowledge."  So how on earth did this come to be emblazoned across national newspapers as a "groundbreaking study"?

 

Having said that, though, my strongest criticism is for the MDPI publishers, who have encouraged an explosion in "special Issues" of their journals, with scant scrutiny of the quality of published articles - each of which brings in an Article Processing Charge of CHF 2600 (around £2,200, or US $2,900). The proliferation of anecdotal reports in the literature gives ammunition to those who wish to promote all kinds of unevidenced treatments: they can point to these "peer reviewed" papers as evidence of scientific respectability.

 

In the longer term, the lax editorial standards that admit pseudoscience into the research literature will further damage the already tarnished MDPI brand, and we can only hope that papers published in the Journal of Personalized Medicine will be discounted by serious journalists. But meanwhile, every practitioner with a dubious product to sell will see this journal as a perfect outlet for their promotional material. 

 

*The headline has been modified and now reads "Autism can be reversed, scientists discover", but it still describes the study as a 'trial', which is isn't.

 

 

References

D’Adamo, C. R., Nelson, J. L., Miller, S. N., Rickert Hong, M., Lambert, E., & Tallman Ruhm, H. (2024). Reversal of autism symptoms among dizygotic twins through a personalized lifestyle and environmental modification approach: A case report and review of the literature. Journal of Personalized Medicine, 14(6), Article 6. https://doi.org/10.3390/jpm14060641

 

Mahapatra, S., Vyshedsky, D., Martinez, S., Kannel, B., Braverman, J., Edelson, S. M., & Vyshedskiy, A. (2018). Autism Treatment Evaluation Checklist (ATEC) Norms: A “Growth Chart” for ATEC score changes as a function of age. Children, 5(2), Article 2. https://doi.org/10.3390/children5020025