Impact of Dry Eye Disease on Vision Quality: An Optical Quality Analysis System Study
Bibliographic record
Abstract
Purpose: We evaluated the relationship between ocular surface clinical tests and quality of vision in patients with dry eye disease (DED). Methods: In this study, 136 eyes of 72 dry eye patients were evaluated retrospectively using the ocular surface disease index (OSDI), measurement of tear film break-up time (TBUT), the Oxford score, Van Bijsterveld score, and Schirmer I test. Quality of vision was assessed with the optical quality analysis system (OQAS) using the objective scatter index (OSI) recorded over 20 seconds without blinking. Correlations between dry eye symptoms and signs, and OSI measurements were evaluated. Results: The OSI and OSI standard deviation (OSI SD) were correlated with TBUT (r = −0.21, P = 0.013 and r = −0.18, P = 0.038, respectively), Oxford score (r = 0.31, P = 0.0002 and r = 0.18, P = 0.032, respectively), and the Van Bijsterveld score (r = 0.33, P = 0.0001 and r = 0.25, P = 0.003, respectively). The OSI also was correlated with the Schirmer test (r = −0.19, P = 0.025), OSDI (r = 0.17, P = 0.04), and the ocular symptoms subscale of the OSDI (r = 0.21, P = 0.01). OSI SD was correlated with the environmental triggers subscale of the OSDI (r = 0.21, P = 0.016). Conclusions: Quality of vision measured with the OQAS was correlated with dry eye symptoms and signs. The OQAS could be a useful tool to better evaluate visual function in patients with DED. Translational Relevance: The OQAS provides a better understanding of patient complaints about alteration of vision quality. It might be useful to integrate this objective system in severity assessments and follow-up of DED, especially for treatment evaluations.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".