Atropine Slows Myopia Progression More in Asian than White Children by Meta‐analysis
Bibliographic record
Abstract
PURPOSE: To conduct a meta-analysis on the effects of atropine in slowing myopia progression and to compare Asian and white children and randomized controlled trials (RCTs) and observational studies. METHODS: Randomized controlled trials and observational studies that assessed the effects of all concentrations of atropine in slowing myopia progression in children were searched from MEDLINE, EMBASE, and the Cochrane Library up to April 2013. Jadad scoring was used to evaluate the quality of RCTs, and the Newcastle-Ottawa Scale was used for observational studies. RESULTS: Four RCTs and seven cohort studies (a kind of observational study) with 1815 children aged 5 to 15 years were included. The children had a baseline refraction of -0.50 to -9.75 diopters (D) and were followed up for 22.0 months (range, 12.0 to 36.5 months). The weighted mean differences in myopia progression in RCTs and cohort studies of Asian children were 0.55 D per year (p < 0.01) and 0.54 D per year (p < 0.001), respectively, and 0.35 D per year (p = 0.01) in cohort studies of white children. Compared with placebo, the risk of fast myopia progression (>1.0 D per year) using atropine was significantly decreased in both RCTs (odds ratio [OR], 0.14; p < 0.01) and cohort studies (OR, 0.08; p < 0.01), and the benefit of slow myopia progression (<0.50 D per year) using atropine was significantly increased in both RCTs (OR, 6.73; p < 0.01) and cohort studies (OR, 22.10; p < 0.01). CONCLUSIONS: Atropine could significantly slow myopia progression in children, with greater effects in Asian than in white children. Randomized controlled trials and cohort studies provided comparable effects.
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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 | 0.016 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.059 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".