Clinical Trials Registries: A Reform that is Past Due
Bibliographic record
Abstract
Several high-profile episodes have recently thrust drug safety and the pharmaceutical industry's practices into the spotlight. Merck's recall of the drug Vioxx, for instance, was a major news event. GlaxoSmithKline's suppression of data linking suicidal behavior among children to Paxil also galvanized tremendous public attention. What differentiates these events from the usual evolving process of scientific knowledge, and marks them with an aura of “scandal,” are questions about the propriety of corporate behavior. Who knew what, and when did they know it? Concerns are growing about the potential for industry sponsors to suppress negative results from clinical trials research. Scientists, medical journal editors, legislators, and the public have called for greater transparency in the conduct of clinical trials and the drug approval process.
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 | 0.374 | 0.508 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.011 | 0.053 |
| Scholarly communication | 0.038 | 0.063 |
| Open science | 0.011 | 0.015 |
| Research integrity | 0.050 | 0.080 |
| Insufficient payload (model declined to judge) | 0.016 | 0.009 |
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".