Consistency of conflict of interest disclosures across two major ophthalmology conferences
Bibliographic record
Abstract
Purpose To quantitatively assess the consistency of conflict of interest (COI) disclosures among presenters at two major ophthalmology conferences and to analyze trends in COI reporting over a span of four years. Design A retrospective cross-sectional study. Participants Presenters at the American Academy of Ophthalmology (AAO) and the American Society of Cataract and Refractive Surgery (ASCRS) annual meetings in 2018 and 2021/2022. Methods Publicly available COI disclosures from presenters at the AAO and ASCRS meetings were extracted and compared. The disclosures of individuals presenting at both AAO and ASCRS were analyzed, focusing on whether COIs were reported consistently across both meetings. Main Outcome Measures The primary outcome was the presence of discrepancies in COI disclosures amongst individuals who presented at the two selected ophthalmology conferences within the same year. Results Among the 260 presenters who participated in both AAO 2021 and ASCRS 2022, 95 (36.5%) had identical disclosures, while 150 (57.7%) exhibited at least one discrepancy. On average, these presenters had 11.23 ± 14.63 disclosures at AAO and 9.88 ± 14.68 disclosures at ASCRS. Similarly, of the 432 presenters at both AAO 2018 and ASCRS 2018, 203 (47.0%) had consistent disclosures, while 213 (49.5%) displayed discrepancies. On average, these presenters had 13.16 ± 19.75 disclosures at AAO and 12.49 ± 15.61 disclosures at ASCRS. Conclusions Significant inconsistencies in COI disclosures were observed among presenters at major ophthalmology conferences within the same year. Nearly half of the presenters exhibited discrepancies in their disclosures, with a notable portion disclosing COIs at one conference but not the other. These findings underscore the need for standardized COI reporting systems with more rigorous verification processes to ensure transparency and trustworthiness in medical conference presentations.
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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 | 0.020 | 0.110 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".