Same-Day Bilateral Intravitreal Anti-Vascular Endothelial Growth Factor Injections: Experience of a Large Canadian Retina Center
Bibliographic record
Abstract
OBJECTIVE: To evaluate the outcomes and complications of bilateral same-day intravitreal anti-vascular endothelial growth factor (anti-VEGF) injections. METHODS: This is a single-center, retrospective study that included 524 eyes of 262 patients who received concomitant bilateral intravitreal anti-VEGF injections in 2016 at St. Michael's Hospital, Toronto. If any of the patients were receiving simultaneous bilateral injections on a regular basis prior to 2016, data pertaining to previous injections were also reviewed. Everyone received bevacizumab, ranibizumab, or aflibercept in an office setting. RESULTS: A total of 9,798 intravitreal anti-VEGF injections (4,899 bilateral injection sessions) were performed in 524 eyes of 262 patients. The average number of bilateral injection sessions per patient was 18.7 ± 14.1. Ranibizumab was the most commonly used anti-VEGF drug (83.8%). The incidence of endophthalmitis was 0.01%, and there were 2 episodes of acute intraocular inflammation among the 9,798 injections (0.02%). All 3 cases occurred after treatment with ranibizumab. There were 2 deaths (0.76%) due to nonvascular causes but no vascular related systemic adverse events were reported. CONCLUSIONS: Same-day bilateral intravitreal anti-VEGF injections present a low rate of complications and are well tolerated by patients. This safe practice may reduce the burden on the health-care system and on the patients.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".