Safety Warnings on the Same Harmful Effects of Medicines: A Comparison of Four National Regulators
Bibliographic record
Abstract
BACKGROUND: Safety advisories provide critical information to clinicians and patients on the harms of medicines. Previous research has shown that national regulators vary in their decisions to issue safety warnings. However, it is not known whether clinicians receive similar information when regulators communicate about the same medicines' harms. AIM: Our aim was to assess whether content provided to clinicians in safety advisories on risk, fatal outcomes, evidence and clinician advice was comparable. METHODS: This retrospective content analysis examines safety advisories issued by the Australian Therapeutic Goods Administration, Health Canada, the United Kingdom Medicines and Healthcare products Regulatory Agency, and the US Food and Drug Administration between 2007 and 2016. Content was extracted from advisories issued on the same medicine and harm (n = 40), including evidence, risk quantification, fatal outcomes and clinician advice. A case study on pioglitazone and bladder cancer illustrates differences in regulatory communications. RESULTS: Variation was seen in the detail and presentation of information on evidence, deaths, risk quantification and advice to clinicians. Specific advice to clinicians was provided in 70% (96/155) of advisories with no significant differences between regulators (p = 0.19). Evidence of harm was presented in 81% (130/160) of advisories and risk quantification in 61% (98/160). The type of evidence presented and directness of information differed however. In the pioglitazone case study, for example, regulators differed in how bladder cancer risks were characterised and advice provided. CONCLUSIONS: Our analysis of safety advisories on the same harms of medicines indicates that while regulators provide similar content elements in safety advisories, risk messages to clinicians vary. This may lead to differences in knowledge and awareness between countries and potentially impact public health outcomes. Further transparency around regulatory decisions on safety advisories is needed.
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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 | 0.056 | 0.235 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".