A descriptive analysis of medicines safety advisories issued by national medicines regulators in Australia, Canada, the United Kingdom and the United States ‐ 2007 to 2016
Bibliographic record
Abstract
PURPOSE: To determine the frequency and characteristics of safety advisories issued by medicines regulatory agencies in Australia, Canada, United Kingdom (UK) and the United States (US). METHODS: This retrospective analysis examines medicines safety warnings issued by the US Food and Drug Administration (FDA), Health Canada (HC), the Australian Therapeutic Goods Administration (TGA) and the UK Medicines and Healthcare products Regulatory Agency (MHRA) from January 1, 2007 until December 31, 2016. A database of warnings obtained from regulators' websites was developed and warnings were classified by communication type, drug, or therapeutic class focus, and the risk discussed. Advisories identifying the same drug or therapeutic class and risk were combined into groups termed "drug-risk issues" for comparisons between regulators. RESULTS: Over this 10-year period, 1441 advisories were identified, with the MHRA issuing the most advisories (MHRA = 469, FDA = 382, HC = 370 TGA = 220). Seventy two percent focussed on single drugs (1034/1441) and 58.7% were alerts (846/1441) posted on the regulators' websites. Diabetes drugs, smoking cessation drugs and immunomodulatory agents were the individual drug types most often subject to safety advisories, while antidepressants, antipsychotics, and proton-pump inhibitors were the top three therapeutic classes. Of 680 identified drug-risk issues, 3.8% (26/680) described a risk of death. By body system, cardiac effects were the most frequent: 10.4% (71/680). CONCLUSION: We found considerable differences in the use of advisories including frequency, communication type, and focus. Disparities in communication about emergent evidence on risks may mean that clinicians and patients in some countries are less well informed about medicine safety concerns than others.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".