A study of warning letters issued to clinical investigators by the United States Food and Drug Administration.
Bibliographic record
Abstract
PURPOSE: This study explores the ethical issues contained in warning letters issued to clinical researchers by the Food and Drug Administration (FDA) in the USA. METHODS: The online FDA Warning Letter Index was reviewed for letters issued to drug and device researchers in the USA and Canada under the violation subject "Clinical Investigator" for the period February 2002 through February 2004. The resultant letters were evaluated for the presence of 7 research ethics themes: deviation from investigational plan; informed consent; adverse event reporting; study reporting; study supervision; institutional review-board approval; and misconduct. RESULTS: Thirty-six FDA warning letters addressing violations of 58 protocols were issued to researchers during the 25 months studied. Researchers performing pulmonary medicine studies received the most warning letters (12), followed by oncology (10) and cardiology (9) researchers. The most common regulatory violations were deviation from the research plan, a flawed or nonexistent consent process, and failure to report or late reporting of adverse events. Three warning letters (8%) mentioned study misconduct, including data fabrication. CONCLUSIONS: Warning letters are informative about good practice, ethics and participant protection in research. As distressing as the content in an FDA warning letter may be to investigators receiving it, that information can become an educational tool for all members of the research team. These letters are also informative as to what the FDA is looking for when they audit clinical trials.
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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 | 0.092 | 0.442 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".