The Impact of Occlusion Therapy on Amblyopia Success Outcomes
Bibliographic record
Abstract
PURPOSE: The recommended amount of occlusion therapy and amblyopia treatment success rates remains controversial. This study explores rates of occlusion therapy success and attempts to address limitations of previous literature. METHODS: A retrospective chart review was performed on patients with occlusion therapy outcomes from 2012 to 2019. Equal visual acuity (VA) or stable VA for three consecutive clinical visits, despite reported good compliance defined outcome VA. RESULTS: Results showed 90.3% of subjects obtained outcome distance VA of 0.3logMAR, 76% ≥0.3logMAR, 35% ≥0.2logMAR, and 6% ≥0.1logMAR in the amblyopic eye following treatment. Sixty-nine percent of the study population obtained equal vision following occlusion therapy. Only initial VA (amblyopic eye) and initial interocular visual optotype difference at distance predicted post-treatment success. CONCLUSION: These results support the conclusion that occlusion therapy, both PTO and FTO, can be effective in treating amblyopia when good compliance is maintained based on parental reports of compliance. Additionally, as VA gain was higher than in previous literature, it is important to continue treatment until VA is equal or three consecutive cycles of stable VA are obtained to ensure maximum VA improvement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".