INTERNAL LIMITING MEMBRANE PEELING FOR DECOMPRESSION OF MACULAR EDEMA IN RETINAL VEIN OCCLUSION: A REPORT OF 14 CASES
Bibliographic record
Abstract
BACKGROUND: Currently, there is no proven treatment for macular edema due to central retinal vein occlusion (CRVO). Moreover, not all cases with macular edema due to branch retinal vein occlusion (BRVO) respond to laser photocoagulation. We postulated that internal limiting membrane (ILM) peeling for decompression of macular edema in cases of retinal vein occlusion would facilitate egress of blood and extracellular fluid out of the inner retinal layers, leading to reduction of macular edema and improvement in visual acuity. METHODS: Fourteen consecutive patients with macular edema due to CRVO or selected cases of BRVO, not eligible for laser photocoagulation, underwent pars plana vitrectomy with removal of preretinal hyaloid and peeling of the ILM stained with indocyanine green dye. RESULTS: In all cases, intraretinal blood and retinal thickening diminished within 6 weeks of surgery. Visual acuity improved in 78.6% of cases. No surgical complications occurred, although one patient developed nuclear cataract 10 months postoperatively. CONCLUSION: Pars plana vitrectomy with ILM peeling in selected cases of CRVO and BRVO showed improvement in visual acuity in this nonrandomized, noncontrolled study. This pilot study adds support to the concept that ILM peeling may of visual benefit when compared with the natural history in these vaso-occlusive diseases.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".