Professional Medical Associations and Their Relationships With Industry
Bibliographic record
Abstract
Professional medical associations (PMAs) play an essential role in defining and advancing health care standards. Their conferences, continuing medical education courses, practice guidelines, definitions of ethical norms, and public advocacy positions carry great weight with physicians and the public. Because many PMAs receive extensive funding from pharmaceutical and device companies, it is crucial that their guidelines manage both real and perceived conflict of interests. Any threat to the integrity of PMAs must be thoroughly and effectively resolved. Current PMA policies, however, are not uniform and often lack stringency. To address this situation, the authors first identified and analyzed conflicts of interest that may affect the activities, leadership, and members of PMAs. The authors then went on to formulate guidelines, both short-term and long-term, to prevent the appearance or reality of undue industry influence. The recommendations are rigorous and would require many PMAs to transform their mode of operation and perhaps, to forgo valuable activities. To maintain integrity, sacrifice may be required. Nevertheless, these changes are in the best interest of the PMAs, the profession, their members, and the larger society.
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 | 0.031 | 0.121 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.039 | 0.007 |
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".