VISUAL OUTCOMES AND COMPLICATIONS AFTER MULTIPLE VITRECTOMIES FOR DIABETIC VITREOUS HEMORRHAGE
Bibliographic record
Abstract
In Brief Purpose To determine the visual outcomes and complications after multiple vitrectomies for repeat diabetic vitreous hemorrhage. Methods A retrospective review during a 4-year period of patients requiring multiple vitrectomies for nonclearing vitreous hemorrhages with at least a 6-month follow-up. Results Of the 38 cases of multiple vitrectomies for diabetic vitreous hemorrhage, the initial visual acuity was 20/50 or better in 5%, between 20/60 and 20/400 in 37%, and worse than 20/400 in 58%. The final visual acuity after the last vitrectomy was 20/50 or better in 25%, between 20/60 and 20/400 in 47%, and worse than 20/400 in 28%. Patients had a mean improvement of 1.08 lines of visual acuity, and a statistically significant difference in logMAR visual acuity was noted when the last corrected visual acuity was compared with baseline acuity by way of paired t-testing. Although a trend toward visual improvement was noted in patients who underwent multiple vitrectomies, multivariate models failed to detect any association between number of surgeries or demographic variables and change in visual acuity. Conclusion Multiple vitrectomies for recurrent diabetic vitreous hemorrhage can have a favorable anatomic outcome while maintaining ambulatory vision. Multiple vitrectomies for recurrent diabetic vitreous hemorrhage can have a favorable anatomic outcome while maintaining ambulatory vision. Patients who underwent surgery had statistically significant improvement in visual acuity, and a trend toward visual improvement was noted in patients who underwent more operations.
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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 | 0.001 |
| 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".