Analysis and optimization of the landing zone parameters of a sclera lens model
Bibliographic record
Abstract
PURPOSE: This study aimed to modify scleral contact lenses to achieve a desired compression standard and to evaluate the effectiveness and reliability of the adjustments. METHODS: In this nonrandomized, noncomparative, and partially masked study Scleral contact lens fittings were analyzed in 20 eyes of 12 patients (50% women, 50% men) diagnosed with keratoconus. Participants were selected based on their need for scleral contact lenses (SCLs), which was determined in complete ophthalmological examinations. Patients were tested with Zenlens scleral contact lenses (Bausch & Lomb, Vaughan, Ontario, Canada). We evaluated compression in the lens support area after one hour of use, excluding cases of peripheral lifting. Photos of the adaptations were sent to five experts for analysis of the quadrants (nasal, temporal, superior, and inferior). We used Fisher's exact test for statistical analysis. RESULTS: The proposed adjustment was highly effective (93.5% correct) in lens delivery (BL=0), with the interrater agreement between doctors ranging from 68.8% to 80.9%. CONCLUSION: The clinical parameters proposed for scleral contact lenses adjustment proved useful and reproducible, enabling their practical application to scleral lens adaptation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".