The adjunctive use of pre-operative intravitreal bevacizumab in the setting of proliferative diabetic retinopathy
Bibliographic record
Abstract
To evaluate the efficacy of pre-operative intravitreal bevacizumab injection on the rate of postoperative vitreous hemorrhage in patients undergoing vitrectomy for complications of proliferative diabetic retinopathy. Consecutive retrospective comparative cohort study. Forty eyes of 37 patients who received pre-operative intravitreal bevacizumab 1.25 mg were compared to a similar group of 44 eyes of 44 patients who had undergone vitrectomy surgery prior to the availability and widespread use of pre-operative intravitreal bevacizumab. The primary outcome measure was the incidence of post-vitrectomy hemorrhage at one week after surgery. Secondary outcome measures included are postoperative vitreous hemorrhage at one month and changes in the best-corrected visual acuity (BCVA). For statistical analysis, the paired Student’s t-test and Fisher’s exact test were used. Four out of 40 eyes (10%) pretreated with intravitreal bevacizumab vs. 12 of 44 eyes (27%) not pretreated with intravitreal bevacizumab had a clinically significant postoperative vitreous hemorrhage at one week. The mean best-corrected visual acuity (BCVA) in bevacizumab group improved from a mean of hand motions to a mean of 20/300 at 1 month (range: 20/25-light perception; p < .001) and mean BCVA in the non-injected group improved from preoperative mean of hand motion to 20/200 at one month follow-up (range: 20/25-no light perception; p < .001). In both groups, 4 patients (12%) needed repeat vitrectomy. There is a trend to reduced incidence of early post-vitrectomy hemorrhage in patients undergoing vitrectomy for complications of proliferative diabetic retinopathy that have been pre-treated with intravitreal bevacizumab 1 week prior to 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".