Consistency of conflict of interest disclosures across two major ophthalmology conferences
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 it