Sponsorship of Medical Textbooks by Drug or Device Companies
Bibliographic record
Abstract
Background: To study whether medical textbooks are sponsored by drug or device companies, and if so, whether they have tried to influence their contents. Methods: Cross-sectional study of the medical textbooks written in Danish for graduate clinical courses at the University of Copenhagen and anonymous web-based survey of editors. For sponsored books, we also contacted the authors. Results: Eleven of 71 medical textbooks (15%) were sponsored. We contacted 11 editors, and for 8 books that had authors that were not editors, we also contacted one author. Ten editors and 5 authors replied. One editor was contacted 5 times by the various sponsors concerning the content of specific chapters and in another case the sponsor had the content of a chapter changed regarding its own drug. Two of the authors noted that they did not know that the book was sponsored. Conclusions: Sponsorship of medical textbooks was not uncommon and may lead to lack of academic freedom. Medical students may be particularly vulnerable to commercial influences, as they have had little or no training in commercial biases and generally believe what they read in textbooks.
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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 | 0.008 | 0.088 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".