Under Disclosure of Conflicts of Interest Is Less Frequent in Senior Authors: A Cross-sectional Review of All Authors Submitting to JAAOS Between 2014 and 2018
Bibliographic record
Abstract
The interactions between physicians and industry are necessary for advancement of clinical practice and improvement in medical devices. Physician-industry relationships also introduces financial conflicts of interest into research publications. Payments to physicians do not inherently introduce bias in research, but failure to disclose potential conflicts of interest can negatively impact the perceived integrity of authors, editors, and journals. The conflict of interest disclosure statement in all articles published in the Journal of the American Academy of Orthopaedic Surgery between 2014 and 2018 were compared to the financial payments indexed in the Center for Open Payments Database. Payment type, magnitude, and payer were obtained for each payment meeting inclusion criteria. Statistical comparisons were made using Mann-Whitney comparisons due to non-normal distribution of payment amounts. 704 articles involving 2596 authors were reviewed, with 1268 authors meeting inclusion criteria. 634 authors had accurate disclosure statements. The total amount of disclosed payments was $169 million, whereas undisclosed payments were $14.2 million. The amount of disclosed payments on a per-author basis, $55,844 ($12,559, $186,129), was significantly greater than undisclosed payments, $2,171 ($568, $7,238). The lowest rates of correct disclosure were in education (29.2%), gifts (38.7%) and honoraria (57.8%). First and middle authors disclosed correctly at a significantly lower rate than last authors. The magnitude of undisclosed payments was significantly lower than disclosed payments, indicating that these payments do not register with authors as significant enough to disclose.
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 | 0.018 | 0.095 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".