Drugs in the news: an analysis of Canadian newspaper coverage of new prescription drugs.
Bibliographic record
Abstract
BACKGROUND: Patients routinely cite the media, after physicians and pharmacists, as a key source of information on new drugs, but there has been little research on the quality of drug information presented. We assessed newspaper descriptions of drug benefits and harms, the nature of the effects described and the presence or absence of other important information that can add context and balance to a report about a new drug. METHODS: We looked at newspaper coverage in the year 2000 of 5 prescription drugs launched in Canada between 1996 and 2001 that received a high degree of media attention: atorvastatin, celecoxib, donepezil, oseltamivir and raloxifene. We searched 24 of Canada's largest daily newspapers for articles reporting at least one benefit or harm of any of these 5 drugs. We recorded the benefits and harms reported and analyzed how such information was presented; we also determined whether clinical or surrogate outcomes were mentioned; if and how drug effects were quantified; whether contraindications, other treatment options and costs were mentioned; and whether any information on affiliations of quoted interviewees and potential conflicts of interest was presented. RESULTS: Our search yielded 193 articles reporting at least one benefit or harm for 1 of the 5 drugs. All of the articles mentioned at least one benefit, but 68% (132/193) made no mention of possible side effects or harms. Only 24% (120/510) of mentions of drug benefits and harms presented quantitative information. In 26% (31/120) of cases in which drug benefits and harms were quantified, the magnitude was presented only in relative terms, which can be misleading. Overall, 62% (119/193) of the articles gave no quantification of the benefits or harms. Thirty-seven (19%) of the 193 articles reported only surrogate benefits. Other information needed for informed drug-related decisions was often lacking: only 7 (4%) of the articles mentioned contraindications, 61 (32%) mentioned drug costs, 89 (46%) mentioned drug alternatives, and 30 (16%) mentioned nondrug treatment options (such as exercise or diet). Sixty-two percent (120/193) of the articles quoted at least one interviewee. After exclusion of industry and government spokespeople, for only 3% (5/164) of interviewees was there any mention of potential financial conflicts of interest. Twenty-six percent (15/57) of the articles discussing a study included information on study funding. INTERPRETATION: Our results raise concerns about the completeness and quality of media reporting about new medications.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.036 | 0.060 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".