Despite Law, Fewer Than One In Eight Completed Studies Of Drugs And Biologics Are Reported On Time On ClinicalTrials.gov
Bibliographic record
Abstract
Clinical trial registries are public databases created to prospectively document the methods and measures of prescription drug studies and retrospectively collect a summary of results. In 2007 the US government began requiring that researchers register certain studies and report the results on ClinicalTrials.gov, a public database of federally and privately supported trials conducted in the United States and abroad. We found that although the mandate briefly increased trial registrations, 39 percent of trials were still registered late after the mandate's deadline, and only 12 percent of completed studies reported results within a year, as required by the mandate. This result is important because there is evidence of selective reporting even among registered trials. Furthermore, we found that trials funded by industry were more than three times as likely to report results than were trials funded by the National Institutes of Health. Thus, additional enforcement may be required to ensure disclosure of all trial results, leading to a better understanding of drug safety and efficacy. Congress should also reconsider the three-year delay in reporting results for products that have been approved by the Food and Drug Administration and are in use by patients.
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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 | 0.136 | 0.251 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".