The Montreal Experience: Impact of Different Orthokeratology Lens Designs on Corneal Treatment Zone Characteristics
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effect of the orthokeratology (OK) lens design, used in the Montreal Experience cohort, on corneal treatment zone characteristics and their relationship to the pupil. METHODS: This retrospective study follows previously published work and refers to the analysis of 4 different OK lenses. Tangential topography maps were obtained at baseline and after 1 month of OK lens wear. The extracted parameters are: distance treatment zone diameter (DTZD (mm)); relative peripheral power (RPP (D)); mid-peripheral width (MPW (mm)); a new concept, the plus power ratio (PPR (%)), corresponding to the coverage of the pupil area by the positive power zones. RESULTS: DTZD and MPW were significantly different between the lens designs (Welch’s ANOVA). (DTZD (OK 1: 3.68 ± 0.46 mm; OK 2: 3.06 ± 0.67; OK 3: 2.83 ± 0.54; OK 4: 3.20 ± 0.53) MPW (OK 1: 1.65 ± 0.21 mm; OK 2: 1.31 ± 0.40 mm; OK 3: 1.46 ± 0.17 mm; OK 4: 1.57 ± 0.17 mm)). PPR was significantly lower in OK 1 (40.1 ± 22.1%) than the other designs (OK 2: 53.8 ± 18.4%; OK 3: 60.3 ± 13.6; OK 4: 54.7 ± 15.3). CONCLUSION: This study shows that the corneal response to OK lens wear varies with lens design. When analyzed, topographic analysis shows that OK 1 is associated with a larger DTZD, which produces a lower PPR. This may explain why previously published results showed significantly faster axial length (AL) progression with this lens.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".