The association between a journal's source of revenue and the drug recommendations made in the articles it publishes
Bibliographic record
Abstract
BACKGROUND: There is evidence to suggest that pharmaceutical companies influence the publication and content of research papers. Most German physicians rely on journals for their continuing medical education. We studied the influence of pharmaceutical advertising on the drug recommendations made in articles published in 11 German journals that focus on continuing medical education. METHODS: We conducted a cross-sectional study of all of the issues of 11 journals published in 2007. Only journals frequently read by general practitioners were chosen. Issues were screened for pharmaceutical advertisements and recommendations made in the editorial content for a specified selection of drugs. Each journal was rated on a five-point scale according to the strength with which it either recommended or discouraged the use of these drugs. We looked for differences in these ratings between free journals (i.e., those financed entirely by pharmaceutical advertising), journals with mixed sources of revenue and journals financed solely by subscription fees. The journals were also screened for the simultaneous appearance of advertisements and recommendations for the same drug within a certain period, which was adjusted for both journal and class of drug. RESULTS: We identified 313 issues containing at least one advertisement for the selected drugs and 412 articles in which drug recommendations were made. Free journals were more likely to recommend the specified drugs than journals with sources of revenue that were mixed or based solely on subscriptions. The simultaneous appearance of advertisements and recommendations for the same drug in the same issue of a journal showed an inconsistent association. INTERPRETATION: Free journals almost exclusively recommended the use of the specified drugs, whereas journals financed entirely with subscription fees tended to recommend against the use of the same drugs. Doctors should be aware of this bias in their use of material published in medical journals that focus on continuing medical education.
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 | 0.025 | 0.192 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".