LAYER-SPECIFIC MICROVASCULAR CHANGES IN IDIOPATHIC EPIRETINAL MEMBRANE
Bibliographic record
Abstract
PURPOSE: To investigate layer-specific microvascular changes in the nerve fiber layer plexus (NFLP) and ganglion cell layer plexus (GCLP) using optical coherence tomography angiography and correlate them with visual outcomes after surgery for idiopathic epiretinal membrane. METHODS: This retrospective study analyzed 47 eyes with idiopathic epiretinal membrane and 30 healthy control eyes. Preoperative and 6-month postoperative best-corrected visual acuity, retinal sensitivity, and optical coherence tomography angiography-derived vascular density were assessed in the NFLP, GCLP, and superficial vascular complex. Diagnostic accuracy was evaluated using receiver operating characteristic curves, and prognostic value was determined through correlation and multiple linear regression analyses. RESULTS: Preoperatively, idiopathic epiretinal membrane eyes showed significantly increased vascular density and perfusion area in the NFLP, GCLP, and superficial vascular complex compared with control eyes ( P < 0.01). Nerve fiber layer plexus parameters demonstrated superior diagnostic accuracy over superficial vascular complex metrics. Preoperatively, increased NFLP vascular metrics were associated with higher best-corrected visual acuity improvement (all P < 0.05). Conversely, greater baseline 6 mm 2 × 6 mm 2 GCLP vascular metrics were associated with a lower best-corrected visual acuity improvement (all P < 0.05). In multivariate analysis, preoperative GCLP vascular density was an independent predictor of both better final postoperative best-corrected visual acuity (β = 1.84, P = 0.045) and higher postoperative retinal sensitivity (β = 0.048, P < 0.001). CONCLUSION: Stratified analysis of the NFLP and GCLP through optical coherence tomography angiography provides distinct and powerful biomarkers for idiopathic epiretinal membrane. Nerve fiber layer plexus metrics excel in diagnosis, whereas preoperative GCLP integrity is a key predictor of postoperative functional recovery, offering more nuanced insights than traditional superficial vascular complex analysis.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".