The truth, but not the whole truth? Call for an amnesty on unreported results of public health interventions
Bibliographic record
Abstract
Lack of time, funds or other resources are the explanations that have been given by clinical researchers for failure to publish all the results of large randomised trials.1 It has been estimated that 40–62% of trials have introduced new variables into the study and/or omitted others.2 This insight has been gained by comparing trial protocols with publications. The International Clinical Trials Registry Platform was established in response to such observations. One goal was to prevent outcome reporting bias, that is, where only a selection of a trial's outcomes are reported, based on the result, leading to a biased view of an intervention's effect.3 Inspired by this, in 2007 the Cochrane Health Promotion and Public Health Field led the call for a register for public health interventions as well, adapted to the diversity of methods used to assess interventions in public health.4 The paper by Pearson and Peters in this …
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.096 | 0.496 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.008 | 0.023 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.066 | 0.077 |
| Insufficient payload (model declined to judge) | 0.012 | 0.011 |
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".