FACTORS ASSOCIATED WITH SPONTANEOUS RELEASE OF VITREOMACULAR TRACTION
Bibliographic record
Abstract
In Brief Purpose: To analyze the factors that may predict the release of vitreomacular traction (VMT) and vitreomacular adhesion. Methods: Retrospective case–control study of sixty-one patients with VMT imaged by optical coherence tomography over at least 3 months. Records from all patients seen at the University of Iowa from January 2012 to September 2013 were screened for the ICD9 code for VMT, vitreomacular adhesion, and epiretinal membrane (379.27 and 362.56). Release of VMT (R-VMT) was defined by resolution of patients' symptoms or traction by optical coherence tomography without surgical intervention or ocriplasmin injection. Individual factors or characteristics were evaluated by chi-square test. Using a binary logistic regression model, the potentially prognostic factors were evaluated for contribution to R-VMT. Results: Of the 61 patients that met entry criteria, 21 (35%) developed R-VMT during optical coherence tomography follow-up, and 40 (65%) did not. Isolated inner retinal distortion without outer retinal involvement was significantly associated with R-VMT (P = 0.01). Vitreous injections were also associated with R-VMT (P = 0.02). Conclusion: Eyes with VMT and isolated inner retinal distortion and those receiving vitreous injections are more likely to develop VMT release without the need for surgical intervention or ocriplasmin treatment. This retrospective case–control study identifies factors that predict the release of vitreomacular traction. Significant factors include isolated inner retinal distortion and concurrent vitreous injections, which predict the subsequent release of vitreomacular traction.
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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 | 0.004 |
| 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 | 0.001 | 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".