Association Between Anisometropia and Amblyopia: A Systematic Review and Meta-analysis Study
Bibliographic record
Abstract
Background: Anisometropia is a common refractive error. It has been associated with an increased risk of developing amblyopia, a condition that can lead to permanent vision loss if left untreated. This study aimed to systematically review and pool the available evidence on the relationship between anisometropia and amblyopia. Methods: A systematic review and meta-analysis was conducted following the PRISMA guidelines. Three main databases were searched for observational studies that addressed the association between anisometropia and the risk of developing amblyopia. The quality of the included studies was assessed using the Newcastle–Ottawa scale. Results: A total of 14 studies were included in the meta-analysis, with a combined sample size of 6,895 participants. Patients with any refractive error had a higher risk of developing amblyopia compared to those without refractive errors (P<0.05). However, the risk of developing amblyopia in patients with refractive errors of less than 1 diopter was relatively small (OR: 1.66, 95% CI: 1.2, 2.12). Conclusion: This systematic review and meta-analysis provide evidence of a significant association between anisometropia and the risk of developing amblyopia. This highlights the importance of early detection and treatment of anisometropia as a potential strategy for preventing amblyopia.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.025 |
| Bibliometrics | 0.005 | 0.007 |
| 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) | 0.004 | 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".