OPPORTUNITIES FOR IMPROVING THE LONG-TERM MANAGEMENT OF KERATOCONUS PATIENTS
Bibliographic record
Abstract
Background and Objective: This study determined whether practitioners specializing in keratoconus (KC) adhere to published guidelines for disease management and to what extent comorbid conditions of dry eye, contact lens tolerance, and psychological consequences of KC are formally assessed as part of long-term management. Materials and Methods: This cross-sectional study used an IRB-approved, Internet-based, REDCap platform. Descriptive statistics are presented. Results: A total of 222 participants qualified for participation. Most 134 (60%) followed young and unstable patients every 6 months and less frequent follow-up examinations for patients with stable findings, with 142 (64%) recommending annual examinations. Scleral lenses were the preferred optical correction method (36%), followed by corneal gas permeable lenses (21%). A total of 118 (55%, n=216) participants recommend crosslinking to any patient with documented disease progression regardless of age. Fewer than 25% of patients were referred for surgical correction of KC. Half of respondents, 114 (51%), reported testing for tear film dysfunction, while 108 (49%) never tested. No participants used a depression screening instrument. Conclusion: Practitioners managing patients with KC largely adhere to current consensus recommendations. This survey identified several potentially high-impact, low-cost improvements to current practice patterns, including screening for dry eye and depression.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".