Rate of change and predictive factors for increasing minus contact lens powers in young myopes
Bibliographic record
Abstract
BACKGROUND: Understanding the factors associated with myopic progression is critical to properly recruit subjects into clinical trials for control of myopia. The purpose of this study is to describe the rate of change in soft contact lens (SCL) power and the associated predictive factors in a young clinical population from the Contact Lens Assessment in Youth study. METHODS: Data from a retrospective chart review of myopic SCL wearers aged eight to 22 years were analysed for rate of progression of myopia and associated characteristics using multivariate methods. RESULTS: Myopic subjects (n = 912) with at least six months of follow-up were observed (4,341 visits, mean follow-up 25 months, 37 per cent hydrogel and 63 per cent silicone hydrogel SCLs). During observation, 36 per cent of subjects experienced a change in soft contact lens power of -0.50 D or more. Significant predictors of future increase in minus lens power were: ages eight to 13 years, shorter time to the first increase in minus power and hydrogel soft contact lens material. The mean annualised increase in minus decreased with age (-0.31D per year for eight to 13 year olds to -0.10 D per year for 20 to 22 year olds, p < 0.0001). Increases in minus were less common among users of silicone hydrogel materials than hydrogel daily disposable lenses after controlling for age (p = 0.039). CONCLUSION: In this retrospective chart review of young soft contact lens wearers, the mean annualised rate of increase in minus soft contact lens power decreased with age, longer time to first increase in power and was greater with hydrogel soft contact lenses. The rates observed were similar to progression rates in prospective myopia clinical trials that employed cylcoplegic autorefraction.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".