INCIDENCE OF ENDOPHTHALMITIS RELATED TO INTRAVITREAL INJECTION OF BEVACIZUMAB AND RANIBIZUMAB
Bibliographic record
Abstract
In Brief Purpose: To report the overall incidence of endophthalmitis associated with office based intravitreal injections of bevacizumab and ranibizumab. Methods: This is a retrospective, consecutive, multicenter case series involving four large clinical sites. Included were all patients receiving at least one injection of intravitreal bevacizumab or intravitreal ranibizumab. Follow-up after each injection was at least 4 weeks. Results: A total of 12,585 injections of intravitreal bevacizumab and 14,320 injections of intravitreal ranibizumab were given during the study period. Infectious endophthalmitis developed in three patients after administration of bevacizumab and in three patients after administration of ranibizumab. Four of these patients were culture positive. Rates of endophthalmitis were 0.02% and 0.02%, respectively, with an overall rate of 0.02%. Conclusion: The rate of endophthalmitis associated with intravitreal bevacizumab and ranibizumab is low, with an incidence of approximately 1 in 4,500 injections. With the advent of anti-VEGF treatments, the risk of endophthalmitis becomes one of great importance. The goal of this multi-center study was to report the per injection incidence of endophthalmitis for intravitreal bevacizumab and ranibizumab. The approximate rate of endophthalmitis was found to be 1 in 4,500 injections.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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) | 0.002 | 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".