US Food and Drug Administration regulatory reviewer disagreements and postmarket safety actions among new therapeutics
Bibliographic record
Abstract
OBJECTIVES: To examine the association between regulatory reviewer disagreements and postmarket safety actions among novel therapeutics approved by the US Food and Drug Administration (FDA) between 2011 and 2015. Disagreements among FDA reviewers regarding the recommendation for a novel therapeutic's approval, its safety, the indicated patient population and/or other parameters of the drug's approval are common. However, the implications of such disagreements-particularly with respect to postmarket safety actions-are poorly understood. DESIGN: Cross-sectional study. SETTING: All novel therapeutics approved by the FDA between January 2011 and December 2015. PARTICIPANTS: None. MAIN OUTCOME MEASURES: Postmarket safety actions defined as new label warnings/increased warning severity, FDA safety communications and safety-related therapeutic withdrawals after the original regulatory approval. RESULTS: Among 174 novel therapeutics approved by the FDA between 2011 and 2015, 42 (24%) had at least one regulatory reviewer disagreement. Altogether, 156 instances of disagreement were observed. Following market approval, a total of 253 postmarket safety actions were taken by the FDA among all new therapeutics, with at least one postmarket safety action identified for 98 (56.3%) of the 174 novel therapeutic approvals. Overall, therapeutics that were the subject of disagreement during the FDA's review had fewer safety actions following approval compared with therapeutics in which no disagreement was observed (38.1% vs 62.1%; RR 0.61, 95% CI 0.41 to 0.92; p=0.006). Therapeutic approvals containing at least one reviewer disagreement also more often carried a black box warning at the point of approval (47.7% vs 31.1%; RR 1.53, 95% CI 1.02 to 2.30; p=0.05). CONCLUSIONS: This investigation of regulatory reviewer disagreements and postmarket safety actions among new therapeutics suggests that disagreements among regulatory reviewers may lead to important pre-emptive actions, potentially mitigating the need for postmarket safety actions to be taken.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".