Comparison Between Dichoptic and Monocular Training Protocols for Treating Monocular Amblyopia: A Meta-Analysis and Systematic Review
Bibliographic record
Abstract
PURPOSE: To review the efficacy of dichoptic and monocular strategies for treating monocular amblyopia, and to examine the factors that determine the degree of recovery from amblyopia. METHODS: Mean and individual participant data (IPD) from studies that used either monocular or dichoptic training methods to treat monocular amblyopic patients were analyzed. A mixed-effects model was used to analyze influential factors. Studies were searched using PubMed, OVID, Cochrane library, and EBM reviews. RESULTS: The mean improvements in visual acuity (VA) for dichoptic and monocular training were 0.153 logMAR and 0.162 logMAR, respectively. In the dichoptic training subgroup, the mean VA improvements were 0.201 logMAR, and 0.145 logMAR for strabismic and anisometropic amblyopia, respectively. In the monocular training subgroup, the mean VA improvements were 0.171 logMAR, and 0.143 logMAR for strabismic and anisometropic amblyopia, respectively. The mean improvements in stereopsis of dichoptic training and monocular training were 1.201 octaves and 1.661 octaves, respectively. Baseline visual acuity of the amblyopic eye and training duration were significant factors influencing visual gains. We found no significant impacts of age, astigmatism, and baseline stereopsis on visual acuity and stereopsis outcomes. CONCLUSIONS: This IPD meta-analysis provides evidence that both monocular and dichoptic training yield different visual acuity outcomes in treating unilateral amblyopia. Subgroup analysis suggests that strabismic amblyopia may respond differently to dichoptic training. Baseline visual acuity of the amblyopic eye and training duration are significant factors influencing visual gains. We believe that a more personalized training program could help restore binocularity in patients with monocular 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 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.023 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".