PREDICTIVE FACTORS OF SPONTANEOUS RELEASE OF VITREOMACULAR TRACTION
Bibliographic record
Abstract
Purpose: To review predictive factors of spontaneous vitreomacular traction (VMT) release. Methods: A systematic literature search was performed on Ovid MEDLINE, Embase, and Cochrane Library. Studies comparing spontaneously released VMT to persistent VMT were included. A meta-analysis was performed using a random effects model, and weighted mean difference, risk ratio (RR), and 95% confidence intervals (95% CI) were reported as appropriate. Results: Of a search of 258 studies, 12 studies were included, from which 272 of 934 eyes (29%) underwent spontaneous release. Mean age was 70.0 years, 37.2% of patients were men, and mean follow-up was 22.0 months. Significant predictive factors for spontaneous release were smaller VMT diameter (n = 177; weighted mean difference = −212.48 µm, 95% CI = [−417.36, −7.60], P = 0.04), epiretinal membrane absence (n = 162; RR = 2.17, 95% CI = [1.18, 3.97], P = 0.01), and right eye involvement (n = 76; RR = 2.10, 95% CI = [1.14, 3.88], P = 0.02). Nonsignificant factors were age, initial best-corrected visual acuity, sex, ocular comorbidity, fellow-eye posterior vitreous detachment, previous intravitreal injection, and VMT classification with focal defined as ≤400 µm. Mean release time was 15.3 months (n = 212). Mean best-corrected visual acuity improved from 0.34 ± 0.21 (Snellen 20/44) to 0.20 ± 0.58 logMAR (Snellen 20/32) postrelease (n = 121). Conclusion: Smaller VMT diameter, epiretinal membrane absence, and right eye involvement may support spontaneous VMT release. If patients have tolerable symptoms, clinicians may consider observation in patients with these predictive factors.
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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.011 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".