EN FACE OPTICAL COHERENCE TOMOGRAPHY AND OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY OF INNER RETINAL DIMPLES AFTER INTERNAL LIMITING MEMBRANE PEELING FOR FULL-THICKNESS MACULAR HOLES
Bibliographic record
Abstract
PURPOSE: To quantitatively and qualitatively evaluate the microvascular and structural abnormalities associated with inner retinal dimpling after internal limiting membrane peeling for full-thickness macular holes using sequential en face optical coherence tomography (OCT) and OCT angiography. METHODS: Thirteen eyes of 13 patients with idiopathic full-thickness macular holes were enrolled in the study. Patients were treated with pars plana vitrectomy, internal limiting membrane peeling, and gas tamponade. Subjects were evaluated preoperatively and at postoperative Months 1, 3, and 6. At each visit, patients underwent a comprehensive ophthalmologic examination, en face OCT and OCT angiography. The morphology and number and proportionate area of inner retinal dimples were analyzed. Vessel density of the superficial vascular complex at all visits was also measured. RESULTS: Inner retinal dimples were identified 1 month after surgery in all cases. The number and proportionate area of inner retinal dimples significantly increased over the follow-up period (P = 0.05). Preoperative vessel density of the superficial vascular complex was 17.9 ± 1.9 and did not change significantly over the follow-up period (P = 0.15). CONCLUSION: Inner retinal dimples are identified with en face OCT as early as the first month after internal limiting membrane peeling for idiopathic full-thickness macular holes and progressively increase in number and proportionate area in the subsequent 3 to 6 months after surgery. This may be the result of progressive deturgescence of the nerve fiber layer in the postoperative period.
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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 | 0.001 |
| 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".