Myopia control efficacy of peripheral defocus soft contact lenses in children and adolescents: A meta-analysis
Bibliographic record
Abstract
Objectives To evaluate the effect of peripheral defocus soft contact lenses (PDSCLs) on controlling myopia progression in children and adolescents, and to compare it with orthokeratology (Ortho-K) and single vision lenses (SVLs). Methods We conducted a systematic search of PubMed, the Cochrane Library, Medline, CNKI, CBM, VIP, and WanFang Data databases for randomized controlled trials (RCTs) and cohort studies that investigated the effects of PDSCLs on myopia control in children and adolescents. The published languages were limited to English and Chinese. The risk bias tool provided by the Cochrane risk-of-bias tool and Newcastle-Ottawa Scale were used to assess the risk bias of included studies of RCTs and CTs. The published biases of included studies were assessed by Egger`s test. Results We included 21 studies, comprising 13 RCTs and 8 cohort studies, with a total of 1337 participants in the PDSCLs group, 428 in the Ortho-K group, and 707 in the SVLs group. The meta-analysis indicated no significant difference between PDSCLs and Ortho-K in controlling the increase of diopter ( MD = 0.01, 95% CI : −0.06, 0.09; P = 0.69) and axial length ( MD = -0.01, 95% CI : −0.02, 0.00; P = 0.28). Compared with SVLs, PDSCLs had a better effect in controlling the increase of diopter ( MD = 0.23, 95% CI : 0.17, 0.28; P < 0.00001) and axial length ( MD = -0.11, 95% CI : −0.12, −0.09; P < 0.00001) in children and adolescents. Conclusions Children and adolescents wearing PDSCLs can achieve better myopia control than those wearing SVLs, and their effect is comparable to that of Ortho-K.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.043 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".