Clinical Safety and Efficacy of Orthokeratology Contact Lenses With Toric Peripheral Curves: A Review of the Literature
Bibliographic record
Abstract
OBJECTIVE: To assess the clinical safety and efficacy of orthokeratology (OK) lenses with toric peripheral curves (TPCs), based on a review of published literature. METHODS: A literature search on OK lenses with TPCs using 11 relevant search term combinations was conducted. Databases included PubMed, Cochrane Online Library, Prospero International Prospective Register of Systematic Reviews, and Embase. The period covered was January 1, 2012, to May 1, 2024. RESULTS: In total, 600 publications were identified in the search databases using the search criteria. Based on titles and abstracts, 52 distinct articles were identified for further review; of these, 16 were determined related to clinical evaluation of OK lenses with TPCs. The published studies involved five different lens models from five different manufacturers: Menicon Z Night Toric (Menicon Co, Ltd, Nagoya, Japan), Euclid Emerald Toric (Euclid Systems Corporation, Herndon, VA), Lucid Night Ortho-K Toric (Lucid Korea, Seoul, Korea), Dual Axis Corneal Refractive Therapy (Paragon Vision Sciences, Gilbert, AZ), and Eyebright Base Curve Aspheric Ortho-K (Eyebright Medical Technology Co, Ltd, Beijing, China). CONCLUSIONS: Published literature suggests that OK lenses with TPCs are effective in treating patients with both myopia and astigmatism, with favorable safety profiles.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".