How do safety warnings on medicines affect prescribing?
Bibliographic record
Abstract
INTRODUCTION: Many adverse effects of medicines only become known after approval, prompting regulatory agencies to issue post-market safety advisories to support safer care. Our team evaluated advisories issued by national regulators in Australia, Canada, Denmark, the United Kingdom, and the United States from 2007 to 2016 inclusive, comparing regulators' decisions to warn, effects on prescribing, doctors' awareness and responses to warnings, relevant regulatory policies, and specific case studies. AREAS COVERED: Based mainly on our research program and a narrative review, this commentary describes how often regulators issue safety advisories and effects on clinical practice. We found extensive differences in decisions to warn, timing and content of warnings. Monitoring advice is often inadequate. The most systematic estimate suggests an average reduction in prescribing of around 6% compared with settings with no advisory. Interviews with doctors suggest limited awareness, uptake, and at times belief in these warnings. EXPERT OPINION: Post-market safety advisories are an important intervention aiming to improve prescribing and use of medicines. However, differing warnings mean that some patients may be exposed to riskier prescribing than others. Better integration of safety information into clinical practice is needed, as well as improved transparency, independence, and public engagement in regulatory decision-making.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".