Withholding Information on Unapproved Drug Marketing Applications: The Public Has a Right to Know
Bibliographic record
Abstract
The Food and Drug Administration (FDA), as a matter of long-standing policy, does not inform the public of instances whereby applications for new drugs or new indications for existing drugs have been rejected by the agency or withdrawn from consideration, nor does it disclose the agency’s analyses of the data submitted with such applications. This lack of transparency is unjustified and prevents patients, researchers, and healthcare providers from gaining insight into why a drug’s application was not approved. The FDA’s policy is particularly troubling in cases where the agency has found a currently marketed drug to be ineffective or unsafe for a newly proposed indication. Disclosure of the FDA’s findings in such cases would promote public health by encouraging healthcare providers to avoid prescribing drugs for unapproved (off-label) uses that the agency has deemed to be potentially dangerous or ineffective. The FDA’s counterpart agencies in Europe and Canada have demonstrated the feasibility of disclosing information on rejected and withdrawn drug marketing applications. The FDA should follow suit and allow the American public to know when a drug is deemed unsafe or ineffective for a certain use.
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 | 0.059 | 0.180 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.026 |
| Scholarly communication | 0.016 | 0.027 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.041 | 0.027 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".