Efficacy of split hours part-time patching versus continuous hours part-time patching for treatment of anisometropic amblyopia in children: a pilot study
Bibliographic record
Abstract
AIM: To compare efficacy of 'split hours part-time patching' and 'continuous hours part-time patching' for the treatment of anisometropic amblyopia. METHODS: We designed a prospective, interventional, non-randomised, comparative pilot study involving children between 4 and 11 years of age with anisometropic amblyopia who were treated with either continuous wear (Group A) or split hours part-time patching (Group B) as per parents wish, after appropriate discussion with the parents. Children were followed-up for the improvement in visual acuity and the compliance at each follow-up visit. RESULTS: 44 and 24 children were recruited in Group A and Group B, respectively (mean ± SD baseline BCVA of the amblyopic eye: 0.99 ± 0.32 and 0.95 ± 0.23 logMAR, respectively). BCVA (adjusted for baseline BCVA and age) at 3 months in Group A (0.59 ± 0.24) was comparable (p=0.08) with that in Group B (0.71 ± 0.24). This was same even at 6 months (0.51 ± 0.25 in Group A and 0.59 ± 0.25 in Group B, p=0.25). The improvement in BCVA at 3 months was also comparable (p=0.06) in Group A (0.39 ± 0.23) and Group B (0.26 ± 0.23). The improvement in BCVA at 6 months was also comparable (p=0.14) in Group A (0.47 ± 0.26) and Group B (0.37 ± 0.26). CONCLUSIONS: Both patching regimens lead to significant and comparable improvement in BCVA in anisometropic amblyopia up to 6 months of follow-up.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".