Predicting Progression of Untreated Macular Pucker Using Retinal Surface En Face Optical Coherence Tomography
Bibliographic record
Abstract
PURPOSE: To review visual outcomes in untreated premacular membrane (PMM) with macular pucker (MP) and evaluate novel predictors of progression. METHODS: In this retrospective observation study, we included 342 eyes with untreated PMM with MP between 2012 and 2015. PMMs were characterized by spectral-domain optical coherence tomography (SD-OCT) imaging based on foveal morphologies, number of retinal contraction centers, central subfield thickness (CST), inner segment ellipsoid band integrity, and photoreceptor deformation index. Additionally, the thickened retina portion was identified by en face OCT and processed digitally to calculate its area. Parameters were retrospectively analyzed using multiple regression analyses to identify associations with change in visual acuity (VA) between initial to final follow-up visit. RESULTS: In 468 eyes with untreated PMM, VA and CST did not change significantly during a mean observation period of 448 days (p = 0.52 and 0.35, respectively). Specifically, VA improved or stayed the same in 80% and worsened by 2 lines or more in 20% of eyes. The only consistent predictor of PMM progression was area of retinal thickening: for every 1 mm2 of retinal thickening at baseline, the odds of having worsened vision at follow-up increased by 6% (OR 1.0606, 95% CI 1.0031-1.1214, p = 0.0387). CONCLUSIONS: The majority of eyes with PMM and good visual function at baseline remain stable or spontaneously improve in VA and macular thickness. Area of retinal thickening as evaluated by en face OCT may be a novel predictor of vision loss in untreated PMM with MP.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".