Undisclosed financial conflicts of interest among authors of American Society of Clinical Oncology clinical practice guidelines
Bibliographic record
Abstract
BACKGROUND: Clinical practice guidelines (CPGs) are crucial to the practice of evidence-based medicine. Declared author financial conflicts of interest (FCOIs) are common in CPGs and have been associated with endorsement of treatment. Less is known about undeclared FCOIs. METHODS: The American Society of Clinical Oncology (ASCO) website was searched to identify all CPGs for systemic therapy published between August 2013 and June 2018. Data on self-reported author FCOIs and funding sources were extracted. The Open Payments database was then searched to identify compensation to CPG authors. Concordance between declared and undeclared but verified FCOIs was assessed with Cohen's κ. RESULTS: For 26 CPGs, 314 nonduplicate authors were identified; 184 of these authors (59%) disclosed FCOIs. Among the remaining 130 authors, data in Open Payments were unavailable for 71 authors (non-US residents or authors affiliated with a nonprofit organization). Among the 59 authors who declared no FCOIs and for whom Open Payments data were available, 55 (93%) had received payment from industry. The κ value for agreement between disclosed and verified FCOIs was 0.092. Among the 243 authors with FCOIs verifiable via Open Payments, 239 (98%) received payment from industry. Thirty-four authors (62%) received more than $1000 in nonresearch funding, and 19 (35%) received more than $5000. Among the 52 first and last authors, 44 (85%) received payment from industry; 14 of these payments (32%) were not declared. CONCLUSIONS: FCOIs among authors of ASCO CPGs are common and are not disclosed by a substantial proportion of authors with Open Payments data. Improved transparency of FCOIs should become standard practice among CPG authors. Professional societies and journal editors need to create a mechanism to verify self-reported FCOIs.
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 | 0.096 | 0.555 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".