Industry-specific Patterns in the Disclosure of Conflicts of Interest in Hand and Upper Extremity Surgery: A Review of the Nerve Allograft Industry
Bibliographic record
Abstract
INTRODUCTION: Industry funding in medicine enhances physician training but can create bias influencing accurate reporting of outcomes. High rates of conflict of interest (COI) disclosure have been found in orthopaedic surgery. However, industry-specific disclosures have not been investigated and small-value compensations previously excluded. Using the nerve allograft industry as a proxy to examine specific patterns of COI between physicians and industries relevant to their publications, we sought to evaluate patterns in industry-specific COI disclosure within the hand and upper extremity surgery literature. METHODS: Literature search for primary studies using nerve allografts in the hand and upper extremity from 2013 to 2021 was conducted. Authors were cross-referenced with their publication's COI statement and payments recorded in the Open Payments Database (OPD). Only payments relevant to the topic or product presented in the publication were included. Payments in all OPD subdivisions were compared. RESULTS: Fourteen studies with 14 first, 72 middle, and 14 senior authors were included. Disclosed and undisclosed payments totaled $2,848,196 and $2,509,397. Only 28% of the authors had completely accurate COI statements. Research and food and beverage comprised the highest and lowest average rates of accurate disclosure (93.8% and 24.9%). The value of accurately disclosed payments was significantly greater on a per-author basis among senior authors ( P < 0.001). Neither the value of undisclosed payments nor the rate of accurate disclosure differed by authorship position ( P = 0.904 and P = 0.350). DISCUSSION: When examined in the context of industries specific to publication, the rate of correct COI disclosure is lower than previously reported with small-value compensation a major contributor. Areas of improvement include the following: (1) All authors should be held accountable for correct disclosure; (2) all forms of financial support should be reported; and (3) journals should independently verify disclosures to the OPD. OPD utilization may help verify correct reporting, especially when the industry is related to the area of study, in the interest of maintaining the highest editorial integrity.
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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 | 0.022 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.024 | 0.027 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".