Nonpublication of trial results for new neurological drugs: A systematic review
Bibliographic record
Abstract
OBJECTIVE: To evaluate nonpublication rates among trials of new successful and unsuccessful neurological drugs. METHODS: "Licensed" drugs consisted of all novel agents receiving US Food and Drug Administration (FDA) licensure 2005-2012 inclusive in seven neurological disorders. "Stalled" drugs included all experimental agents tested in the same domains that had at least one completed phase III trial in the same time frame, but failed to receive FDA approval. Trials of these drugs were included in our sample if their primary outcome collection occurred before October 1, 2010. We determined the publication status of eligible trials using searches of clinicaltrials.gov, Google Scholar, PubMed, Embase, sponsor websites, and direct electronic query of trial contacts and sponsors. The primary outcome was time to journal publication (or results reporting in other media) after study completion. RESULTS: The adjusted hazard ratio for publication was 1.79 (95% confidence interval, 1.20-2.67) in favor of licensed drugs. Based on the criteria for nonpublication in this report, 14,092 and 33,882 volunteers participated in unpublished trials of licensed and stalled neurological drugs, respectively. Result data were not publicly available in any form for 10% (16 of 163) and 46% (94 of 203) of trials of licensed and stalled drugs, respectively. INTERPRETATION: Results of trials for stalled drugs are heavily under-reported. This deprives research and care communities of evidence about pathophysiology, drug class effects, and the value of surrogate endpoints in trials. Ann Neurol 2017;81:782-789.
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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 | 0.203 | 0.548 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.016 | 0.018 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".