Author Financial Conflicts of Interest, Industry Funding, and Clinical Practice Guidelines for Anticancer Drugs
Bibliographic record
Abstract
PURPOSE: Clinical practice guidelines (CPGs) and consensus statements (CSs) are used to apply evidence-based medicine or expert recommendations to clinical practice. Here we explore author financial conflicts of interest (FCOIs), sources of guideline funding, and their relationship with endorsement of specific drugs. METHODS: An electronic search of MEDLINE was conducted to identify CPGs and CSs for common solid cancers published between January 2003 and October 2013. The search was restricted to articles evaluating systemic therapy. We extracted data on self-reported author FCOIs, funding sources, use of manuscript writers, and endorsement of specific drugs in the abstract of the article. RESULTS: Of 142 articles evaluated, 64% were CPGs, and 36% were CSs. The proportion of articles reporting FCOIs improved from 11% in 2003 to 93% in 2013 (P for trend < .001). Only 45% of articles explicitly reported funding sources. Of these, 65% disclosed partial or full industry sponsorship. Use of manuscript writers was declared in 13%, but many articles did not explicitly report the role of authors in the writing of the manuscript. Endorsement of specific drugs was significantly associated with author FCOIs (odds ratio [OR], 7.29; P = .001), but not with industry funding (OR, 0.95; P = .37). CONCLUSION: Reporting of FCOIs in CPGs and CSs has improved over time. Despite prevalent funding of guideline development by industry, such funding is not associated with endorsement of specific drugs. Author FCOIs are prevalent, and endorsement of a specific drug seems to be more common when authors have FCOIs with the pharmaceutical company marketing that drug.
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 | 0.067 | 0.403 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".