Commentary: What conflicts of interest tell us about autism intervention research—a commentary on Bottema‐Beutel et al. (2020)
Bibliographic record
Abstract
Bottema-Beutel, Crowley, Sandbank, and Woynaroski (Journal of Child Psychology and Psychiatry, 2020) have performed a Herculean and invaluable task in their investigation of conflicts of interest (COIs) in nonpharmacological early autism intervention research. Drawing on a meta-analysis of 150 articles reporting group designs, they found COIs in 105 (70%), only 6 (5.7%) of which had fully accurate COI statements. Most reports had no COI statements, but among the 48 (32%) which did, the majority of those declaring no COIs had detectable COIs (23 of 30; 77%). Thus, COI reporting in the literature examined is routinely missing, misleading, and/or incomplete; accurate reporting is the exception rather than the rule. That 120 of the 150 reports were published in 2010 or later, compared to 6 pre-2000, tells us this is not about practices confined to decades past. Instead, it reflects and is a telling indictment of established standards in autism intervention research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.071 | 0.056 |
| Insufficient payload (model declined to judge) | 0.006 | 0.009 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".