Systematic assessment of opioid advertisements in general medical journals
Bibliographic record
Abstract
OBJECTIVE: To systematically examine the content of opioid-related advertisements. DESIGN: Content analysis and quantitative assessment. SETTING: North America. PARTICIPANTS: . MAIN OUTCOME MEASURES: Number of advertisements, nature of the claims made, and quality of cited evidence in the advertisements. RESULTS: Opioid advertisements composed 89 of the 3173 pharmaceutical advertisements in 210 journal issues searched. Seventy-three advertisements were able to be obtained for analysis. Thirty-four (46.6%) did not mention the addictive potential of opioids, and 54 of 73 (74.0%) did not mention the possibility of death. All referenced studies in advertisements were funded by pharmaceutical organizations or had pharmaceutical company employees as authors. No advertisements cited high-quality evidence. CONCLUSION: Many claims of the effectiveness and safety of opioids were published in medical journals through advertisements. Advertisements did not usually mention key negative information about opioids. Although the extent to which these advertisements directly influenced the development of the opioid crisis in North America is unknown, the marked omission of important detrimental effects of opioids may have played a role. Further efforts to restrict opioid marketing may be warranted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.066 | 0.345 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.072 | 0.039 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 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".