Extending the US Food and Drug Administration’s Postmarket Authorities
Bibliographic record
Abstract
Importance: The US Food and Drug Administration (FDA) has expansive regulatory flexibility regarding the quality and quantity of evidence it deems sufficient to approve new drugs, which has been increasingly used to grant approval based on less certain evidence of benefit. However, the FDA's regulatory flexibility with respect to standards for approval has not been matched by sufficient stringency in its exercise of postmarket safeguards, including the FDA's authority and willingness to require confirmation of benefit through postmarket efficacy studies or to withdraw approval when benefit is not confirmed. Objective: To identify and evaluate opportunities for the FDA to extend its authority to require postmarket efficacy studies and use expedited withdrawal procedures for drugs approved despite substantial residual uncertainty outside the accelerated approval pathway. Evidence: The FDA's current approaches to regulatory flexibility with respect to standards for drug approval; examples of shortcomings in the postmarket period; existing statutes and regulations governing the scope of the FDA's authority to impose and enforce postmarket study requirements; and recent legislative reform and agency action regarding the accelerated approval pathway. Findings: Drawing on the broad language of the federal Food, Drug, and Cosmetic Act, the FDA could independently extend its core accelerated approval authorities-required postmarket efficacy studies and expedited withdrawal procedures-to any drug approved with substantial residual uncertainty regarding benefit, such as those supported by a single pivotal trial. To avoid exacerbating existing problems that have become evident during the past 3 decades of experience using the accelerated approval pathway, however, the FDA must ensure that postmarket studies are well designed and completed quickly, while compelling expedited withdrawal when needed. Conclusions and Relevance: Under current FDA approaches to drug approval, patients, clinicians, and payers may be left with little confidence about a drug's benefit not only when it first enters the market but also for an extended period thereafter. If policy makers continue to favor earlier market access over evidentiary certainty, flexible approvals must be matched by more expansive use of postmarket safeguards, an approach possible within the FDA's existing legal authorities.
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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 | 0.118 | 0.193 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.014 | 0.017 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".