editorials Collaborating with pharmaceutical research Family physicians beware!
Bibliographic record
Abstract
The Canadian Medical Association (CMA) has recently updated its policy concerning phy-sicians and the pharmaceutical industry.1 The policy is meant to serve as a guide to physicians, residents, medical students, and medical organi-zations as they interact with the for-profit health care industry. In the past, this interaction primarily involved the pharmaceutical industry, but the policy’s principles apply to relationships with other com-mercial organizations, including the information technology industry and manufacturers of health-related products. The policy first proposes some general prin-ciples, then expands on physicians ’ participation in industry-sponsored research and surveillance studies. It includes a section on the complex (and sometimes controversial) reality of continuing medical education and professional development sponsored by the industry. While some will criticize this policy as being too restrictive and out of touch with current realities, others will appreciate its emphasis on protecting patient-physician relationships. Some might even find the policy too lenient, but these various views only highlight the historical and current complex-ity of the collaboration between physicians and industry, collaboration that is unlikely to end soon. The College of Family Physicians of Canada’s Committee on Ethics has developed a work-in-progress for use by those teaching ethics in family medicine training programs. An emphasis on ethics will be supported and formalized in future editions
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 | 0.011 | 0.068 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.048 | 0.030 |
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".