Bibliographic record
Abstract
Background Selective reporting of outcomes within a published study based on their nature or direction can result in systematic differences between reported and unreported data. Direct evidence of outcome reporting bias is limited to case reports. Objective To study empirically the nature of outcome reporting bias in randomised controlled trials (RCTs). Methods Three cohorts of RCTs were identified: PubMed-indexed RCTs published in December 2000; trial protocols approved by a Danish ethics committee from 1994-95; and trial protocols funded by a government agency in Canada from 1990-98. Data on reported and unreported outcomes were recorded from all trial publications and a survey of authors. An outcome was considered incompletely reported if insufficient data were presented for meta-analysis. Odds ratios relating the completeness of outcome reporting to statistical significance were calculated for each trial, and then pooled using a random effects meta-analysis. Protocols and publications were also reviewed for discrepancies in primary outcome reporting. Results 519 trials with 10,557 outcomes, 102 trials with 3613 outcomes, and 48 trials with 1390 outcomes were identified for the PubMed, ethics committee, and funding agency cohorts respectively. 22%-35% of outcomes per parallel group study were, on average, incompletely reported for meta-analysis. Fully reported outcomes had a two- to three-fold higher odds of being statistically significant compared to incompletely reported outcomes. The most common reasons given for omitting outcomes included a lack of clinical importance, lack of statistical significance, and space constraints. Major discrepancies between primary outcomes in protocols and publications were found in one half of trials. Discussion and conclusions The reporting of trial outcomes is frequently inadequate for meta-analysis; is biased to favour statistical significance; and is inconsistent with pre-specified protocol outcomes. Unacknowledged modifications to outcomes specified in trial protocols constitute scientific misconduct. Meta-analyses may therefore produce inflated and unreliable estimates of treatment effect.
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 | 0.605 | 0.835 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.019 | 0.015 |
| Bibliometrics | 0.026 | 0.031 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.013 | 0.009 |
| Research integrity | 0.014 | 0.011 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".