Publication bias in the medical literature: A review by a Canadian research ethics board
Bibliographic record
Abstract
BACKGROUND: We reviewed the publication record of all protocols submitted to the Capital District Health Authority Research Ethics Board (REB) in Halifax, Nova Scotia, for the period 1995-1996. Because of a heightened awareness of the issue, we hypothesized that there would be less publication bias (a failure to report negative results) and a higher publication rate from completed studies, than previously reported. METHODS: Closed studies were identified from the REB database. Publications were identified by the investigators, requests from sponsors, and a literature review. For each publication, we identified authors, title, journal, number of subjects enrolled, and whether or not the publication was a report of a randomized clinical trial. Comparisons were done using a Student's t test, the Chi-square statistic, or Fisher's exact test as appropriate. RESULTS: From the database of closed studies, 106 remained unpublished, while completed investigations resulted in 84 publications (44% publication rate). The median time to publication was 32.5 months. Publication of statistically significant results occurred in 71/84 trials. Publication of protocols submitted by departments ranged from 91% (anesthesia; 10/11) to 25% [nursing; 2/8 (P<0.05)]. Trials investigating new drugs in Phase 3 or 4 studies were more likely to be published than trials investigating agents in Phase 1 or 2 (P<0.05), and were less likely to be published if sponsored by a pharmaceutical company (P<0.05). CONCLUSIONS: Publication bias continues to be a problem, particularly for early phase investigative studies. Our results suggest that a different approach is required to reduce publication bias. The role that REBs and peer-reviewed journals might play requires further exploration.
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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 | 0.308 | 0.491 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.053 | 0.051 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".