Industry‐Reported Financial Relationships Among American Ophthalmology Society Board Members
Bibliographic record
Abstract
BACKGROUND: To assess financial disclosures of American ophthalmology society board members by comparing self-reported disclosures with industry-reported payments and examining characteristics linked to larger financial relationships. METHODS: In this retrospective, cross-sectional study, we assessed all governance board members from American ophthalmology societies in December 2022. Board composition was identified from society websites, payment data from the Open Payments database, and conflict of interest (COI) policies from IRS Form 990 filings. Outcomes included concordance between self- and industry-reported disclosures, payment values, gender and subspecialty differences and academic characteristics. RESULTS: Among 871 board members from 66 societies, 566 (65.0%) had industry-reported relationships, yet only 22 (2.5%) disclosed COIs on society websites. In 2022, 13 187 payments totaling $57.8 million were reported, with 79.5% related to research. Most societies reported internal COI policies (77.8%) and annual disclosure requirements (75.6%) via IRS filings. Men received significantly higher median payments than women ($217.5 vs. $43.3; p < 0.001). Retina specialists accounted for the largest share of payment value (55.3%), while paediatric ophthalmologists received the least (0.4%). Board members with research payments had higher academic productivity (median h-index: 19 vs. 8; p < 0.001). CONCLUSIONS: Public reporting of board members' financial relationships on ophthalmology society websites was uncommon, likely reflecting differences in society-level disclosure practices rather than individual nondisclosure. These findings underscore an opportunity for societies to enhance transparency by adopting more consistent, transparent COI reporting practices in ophthalmology governance.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".