Regulatory post‐market drug safety advisories on cardiac harm: A comparison of four national regulatory agencies
Bibliographic record
Abstract
Information on rare adverse effects is often limited when a medication is initially approved for marketing. Medicines regulators use safety advisories to warn health professionals and consumers about emerging harms. This study aimed to identify characteristics and advice provided in cardiac safety advisories released by regulators in Australia, Canada, the United Kingdom, and the United States. This was a retrospective study of safety advisories about cardiac-related adverse events issued by these four international medicines regulators between 2010 and 2016. A descriptive overview was followed by a more detailed content analysis, focusing on recommended actions for health professionals, including monitoring advice. For the latter, we applied the systematic information for monitoring (SIM) scale to assess adequacy. Over this period, 164 safety advisories about cardiac harms were issued by the four regulators. There were 61 drugs with advisories of cardiac risk, only 9 (14.7%) of which had advisories from all regulators in countries where the drug was approved. The most common adverse events were cardiac arrhythmias (n = 97, 59.1%) and coronary artery disorders (n = 39, 23.8%). The most frequent advice to prescribers was to monitor patients (n = 74, 45.1%), although only 41.2% of these advisories provided detailed advice on how monitoring should occur. We found many differences in the decision to warn and the advice provided. Patient monitoring was most often recommended, but key information such as frequency or thresholds for action was often lacking. Healthcare professionals and consumers need consistent information about rare serious harms so that they can make informed decisions.
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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 | 0.028 | 0.097 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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, 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".