Media coverage of drug regulatory agencies' safety advisories: A case study of citalopram and denosumab
Bibliographic record
Abstract
AIMS: Drug regulators issue safety advisories to warn clinicians and the public about new evidence of harmful effects of medicines. It is unclear how often these messages are covered by the media. Our aim was to analyse the extent of media coverage of two medicines that were subject to safety advisories from 2007 to 2016 in Australia, Canada, the United Kingdom and the United States. METHODS: Two medicines widely used to treat mental health or physical conditions were selected: citalopram and denosumab. Media reports were identified by searching LexisNexis and Factiva. Reports were included if they stated at least one health benefit or harm. A content analysis of the reports was conducted. RESULTS: In total, 195 media reports on citalopram and 239 on denosumab were included. For citalopram, 43.1% (84/195) of the reports mentioned benefits, 85.6% (167/195) mentioned harms and 9.7% (19/195) mentioned the harm described in the advisories (cardiac arrhythmia). For denosumab, 94.1% (225/239) of the reports mentioned benefits and 39.7% (95/239) mentioned harms. The harms described in the advisories were rarely mentioned: 10.9% (26/239) of the reports mentioned osteonecrosis and ≤5% mentioned any of the other harms (atypical fractures, hypocalcaemia, serious infections and dermatologic reactions). CONCLUSIONS: We found limited media coverage of the harms highlighted in safety advisories. Almost two-thirds of the media stories on denosumab did not include any information about harms, despite the many advisories during this time frame. Citalopram coverage covered harms more often but rarely mentioned cardiac arrhythmias. These findings raise questions about how to better ensure that regulatory risk communications reach the general public.
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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 | 0.008 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".