Secret safety warnings on medicines: A case study of information access requests
Bibliographic record
Abstract
PURPOSE: There has been less attention to the transparency of postmarket evidence of harmful effects of medicines than of premarket clinical trial data. This is a case study of requests for Australian "direct health professional communications" (DHPCs). These letters are used by regulators and manufacturers to inform clinicians of emergent evidence of harm. DHPCs are not made public by Australia's Therapeutic Goods Administration (TGA). METHODS: We requested all DHPCs sent out in Australia from 2007 to 2016 inclusive for 207 drugs that were subject to safety advisories over this decade in Canada, the United Kingdom, and/or the United States. We contacted 39 manufacturers (February to May 2018), with repeat requests to nonrespondents, and a follow-up freedom-of-information (FOI) request to the TGA. RESULTS: Fifteen companies provided information, either sending DHPCs (n = 4, on five drugs) or affirming none were sent out (n = 11). The remaining 24 of 39 (62%) companies did not provide DHPCs: nine (23%) refused the request, often citing commercial confidentiality; the rest provided no answer despite repeat requests. In total, we had no information for 170 of 207 (82%) of the drugs. Our FOI request to the TGA was unsuccessful. CONCLUSIONS: Our experience highlights unacceptable secrecy concerning safety warnings previously sent to thousands of Australian clinicians. In the absence of explicit regulatory policy supporting disclosure, companies differed in their response. These letters warn of serious and often life-threatening harm and guide safer care; full ongoing public access is needed, ideally in searchable online databases.
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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 | 0.022 | 0.127 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".