Factors Influencing the Success of Treatment in Anisometropic Amblyopia
Bibliographic record
Abstract
Purpose: To examine factors influencing successful resolution of amblyopia in children with hyperopic and astigmatic anisometropia presenting to a pediatric ophthalmology practice in London, Ontario. Methods: A retrospective chart review was conducted to identify children treated for hyperopic and astigmatic anisometropia from 2008-2016. 39 children ages 12 years and under with hyperopic and astigmatic anisometropia were included. Information regarding referral pattern, presenting findings and outcomes was collected. Presenting degree of anisometropia, compliance, age at presentation and initial visual acuity (VA) were all statistically analyzed to determine effect on final VA. Results: The mean age at referral to pediatric ophthalmology was 5.2 years. 47% presented with dense amblyopia, with the poorer eye having a VA of 6/30 or worse. 51% of children were successfully treated, with a final VA of 6/9 or better in the worse eye, and 5% of children had residual dense amblyopia. Presenting magnitude of anisometropia was the only factor found to have a significant effect on successful treatment, as for every one diopter decrease in magnitude of anisometropia, there was a 40% higher odds of achieving a final VA of 6/9 or better (point estimate 0.62, 95% CI 0.39-0.97, p=0.03). Age at presentation, presenting VA and compliance to treatment all had no significant outcome on treatment success. Conclusions: Magnitude of anisometropia was found to be the only significant factor that influences the final visual acuity of children, confirming the necessity for early detection of amblyogenic refractive errors.
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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 | 0.000 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".