Warning Letters to Sponsor-Investigators at Academic Health Centres – The Regulatory “Canaries in a Coal Mine”
Bibliographic record
Abstract
PURPOSE: This study highlights Warning Letter (WL) findings issued to sponsor-investigators (S-Is) by the Food and Drug Administration (FDA). METHODS: The online index of WLs issued from October 1, 2007 through September 30, 2012 was reviewed [1]. Through a manual screening process, letters were evaluated if specifically issued to 'clinical investigators', 'sponsors' or 'sponsor-investigators'. A particular focus was given to S-Is at Academic Health Centres (AHCs). Each letter was scored for the presence of violations in 40 general regulatory categories. RESULTS: A review of FDA WLs issued over a five-year period (FDA Fiscal Years 2008-2012) revealed that WLs to S-Is represent half of the WLs issued to all sponsors (16 of 32 letters). A review of these letters indicates that S-Is are not aware of, or simply do not meet, their regulatory responsibilities as either investigators or sponsors. In comparing total sponsor letters to those of S-Is, the most cited violation was the same: a lack of monitoring. A review of publicly available inspection data indicates that these 16 letters merely represent the tip of the iceberg. CONCLUSION: This review of the WL database reveals the potential for serious regulatory violations among S-Is at AHCs. Recent translational funding initiatives may serve to increase the number of S-Is, especially among Academic Health Centres (AHCs) [2]; thus, AHCs must become aware of this S-I role and work to support investigators who assume both roles in the course of their research.
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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 | 0.045 | 0.222 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".