Postoperative changes in the retinal thickness and volume after vitrectomy for epiretinal membrane and internal limiting membrane peeling
Bibliographic record
Abstract
The aim of the study was to investigate the thickness and volume profiles of each retinal layer in postoperative patients with epiretinal membranes.Twenty-four patients who underwent pars plana vitrectomy with internal limiting membrane (ILM) peeling for epiretinal membrane were included. The best corrected visual acuity, thickness, and volume were recorded from the medical records through a retrospective review. Spectral domain optical coherence tomography was used to measure the average thickness and volume of each retinal layer before surgery and 6 months postoperatively.All 24 patients were monitored for 60 months after surgery. In all Early Treatment Diabetic Retinopathy Study (ETDRS) subfields, the thickness and volume of the retinal nerve fiber layer and the inner retinal layer decreased significantly. In contrast, the thickness and volume of the ganglion cell layer, inner nuclear layer, inner plexiform layer, and outer plexiform layer only decreased in some ETDRS subfields. Finally, there was no significant change in the thickness or volume of the outer nuclear layer (ONL), retinal pigment epithelium (RPE), and photoreceptor layers in all ETDRS subfields.The thickness and volume of the inner retina layer decreased significantly after pars plana vitrectomy using ILM peeling. However, there was no significant change in the thickness and volume of the outer retinal layers (ONL, RPE, and photoreceptor) after surgery.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".