MétaCan
Menu
Back to cohort
Record W1493576894 · doi:10.36834/cmej.36527

Sponsorship of Medical Textbooks by Drug or Device Companies

2010· article· en· W1493576894 on OpenAlexvenueno aff
Andreas Lundh, Peter C Gøtzsche

Bibliographic record

VenueCanadian Medical Education Journal · 2010
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmaceutical industry and healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsMedical educationDanishMedicinePsychologyLibrary scienceComputer science

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.020
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.343
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.008
Insufficient payload (model declined to judge)0.3430.000

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.

Opus teacher head0.277
GPT teacher head0.555
Teacher spread0.278 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Medical Education JournalSame topicPharmaceutical industry and healthcareFrench-language works237,207