ECTOPIC INNER FOVEAL LAYER CLASSIFICATION SCHEME PREDICTS VISUAL OUTCOMES AFTER EPIRETINAL MEMBRANE SURGERY
Bibliographic record
Abstract
PURPOSE: To evaluate the ectopic inner foveal layer (EIFL) staging scheme as a visual prognostic factor for patients undergoing epiretinal membrane (ERM) surgery. METHODS: Retrospective study of 88 pseudophakic patients with diagnosis of idiopathic ERM who underwent ERM surgery with a minimum follow-up of 12 months. Preoperative and postoperative EIFL staging was correlated with the final best-corrected visual acuity (BCVA). As a secondary outcome, evaluation of the proportion of patients achieving final best-corrected visual acuity ≥20/40 in each stage was assessed. RESULTS: Based on the EIFL staging scheme, of 88 pseudophakic eyes analyzed, 24 (27.4%) were diagnosed as Stage 2 ERM, 45 (51.1%) as Stage 3 ERM, and 19 (21.5%) as Stage 4 ERM preoperatively. At the final follow-up visit, 70.8% of eyes with Stage 2 showed an improvement in EIFL staging scheme, while 68% of eyes in Stage 3 and 4 remained the same. The final best-corrected visual acuity significantly improved with all EIFL stages (P = <0.05). However, earlier stages were associated with better visual outcomes both preoperatively and postoperatively (Stage 2 > Stage 3 > Stage 4 P < 0.001). Final best-corrected visual acuity ≥20/40 was reached in 91.7% of eyes with Stage 2, 42.3% with Stage 3, and 5.2% with Stage 4. CONCLUSION: The EIFL staging scheme is an easy, fast, and reproducible method to evaluate visual prognosis with ERM surgery. Surgery on Stage 2 ERM results in significantly better visual outcomes and a greater chance of reversibility in anatomical changes.
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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 | 0.000 | 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".