An algorithm for the preoperative diagnosis and treatment of ocular surface disorders
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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