Low-dose atropine 0.01% for the treatment of childhood myopia: a pan-India multicentric retrospective study
Bibliographic record
Abstract
Objective The objective of this study was to assess the efficacy of low-dose atropine 0.01% in controlling myopia progression among Indian children over a 2-year period. Methods This retrospective study, conducted across 20 centres in India, monitored the progression of myopia over 2 years after initiating treatment with 0.01% atropine eye drops. This included children between 6 and 14 years with baseline myopia ranging from −0.5 D to −6 D, astigmatism≤−1.5 D, anisometropia ≤ −1 D and documented myopia progression of ≥0.5 D in the year prior to starting atropine. Subjects with any other ocular pathologies were excluded. Results A total of 732 children were included in the data analysis. The mean age of the subjects was 9.3±2.7 years. The mean myopia progression at baseline (1 year before starting atropine) was −0.75±0.31 D. The rate of myopia progression was higher in younger subjects and those with higher baseline myopic error. After initiating atropine, myopia progression significantly decreased to −0.27±0.14 D at the end of the first year and −0.24±0.15 D at the end of the second year (p<0.001). Younger children (p<0.001) and higher baseline myopia (p<0.001) was associated with greater myopia progression and poor treatment response (p<0.001 for both). Conclusion Low-dose atropine (0.01%) effectively reduces myopia progression over 2 years in Indian children.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".