Questionable content of an industry-supported medical school lecture series: a case study
Bibliographic record
Abstract
BACKGROUND: Medical schools are grappling with how best to manage industry involvement in medical education. OBJECTIVE: To describe a case study of industry-supported undergraduate medical education related to opioid analgesics. METHOD: Institutional case study. RESULTS: As part of their regular curriculum, Canadian medical students attended pain pharmacotherapy lectures that contained questionable content about the use of opioids for pain management. The lectures were supported by pharmaceutical companies that market opioid analgesics in Canada and the guest lecturer was a member of speakers bureaus of the same companies. These conflicts of interests were not fully disclosed. A reference book that reinforced some of the information in the lectures and that was paid for by a sponsoring company was made available to students. This is the first report of an association between industry sponsorship and the dissemination of potentially dangerous information to medical students. CONCLUSIONS: This case demonstrates the need for better strategies for preventing, identifying and dealing with problematic interactions between the pharmaceutical industry and undergraduate medical education. These might include the avoidance of unnecessary conflicts of interest, more disclosure of conflicts, an open process for dealing with recognised problems and internationally harmonised conflict of interest policies.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.008 | 0.006 |
| 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".