An algorithm for the preoperative diagnosis and treatment of ocular surface disorders
Bibliographic record
Abstract
Any ocular surface disease (OSD), but most commonly, dry-eye disease (DED), can reduce visual quality and quantity and adversely affect refractive measurements before keratorefractive and phacorefractive surgeries. In addition, ocular surgery can exacerbate or induce OSD, leading to worsened vision, increased symptoms, and overall dissatisfaction postoperatively. Although most respondents of the recent annual American Society of Cataract and Refractive Surgery (ASCRS) Clinical Survey recognized the importance of DED on surgical outcomes, many were unaware of the current guidelines and most were not using modern diagnostic tests and advanced treatments. To address these educational gaps, the ASCRS Cornea Clinical Committee developed a new consensus-based practical diagnostic OSD algorithm to aid surgeons in efficiently diagnosing and treating visually significant OSD before any form of refractive surgery is performed. By treating OSD preoperatively, postoperative visual outcomes and patient satisfaction can be significantly improved.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".