Bibliographic record
Abstract
pharmaceutical promotion: What happens when companies breach advertising guidelines? Joel Lexchin, MD SOME OR ALL OF THE PROMOTIONAL ACTIVITIES of pharmaceutical companies are typi-cally governed through self-regulatory codes administered by industry associations. However, the conflicts between the commercial objectives and the ethical and sci-entific goals of promotion can potentially lead to serious weaknesses in the way in which these codes are enforced. This paper focuses on 5 critical aspects involved in the enforcement of codes governing pharmaceutical promotion: mechanisms for recognizing violations, composition of monitoring committees, sanctions for code violations, the quantity and quality of information in reports issued about com-plaints and code violations, and the circulation these reports receive. The Code of Marketing Practices of the Pharmaceutical Manufacturers Association of Canada (PMAC) has serious weaknesses in all of these areas. Although the Pharmaceutical Advertising Advisory Board’s Code of Advertising Acceptance avoids many of the deficiencies of the PMAC code, it, too, has weaknesses. Proposals for strengthening
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 | 0.078 | 0.214 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.011 | 0.016 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".