Endophthalmitis Prophylaxis in Cataract Surgery: Overview of Current Practice Patterns Around the World
Bibliographic record
Abstract
BACKGROUND: Acute-onset postoperative endophthalmitis after cataract surgery remains a rare but important cause of visual loss. There is no global consensus regarding the optimal strategies for prophylaxis of endophthalmitis and practices vary substantially around the world, especially with respect to the use of intracameral antibiotics. The European Society of Cataract & Refractive Surgeons in a randomized clinical trial (2007) reported an approximately 5-fold reduction in endophthalmitis rates associated with the use of intracameral cefuroxime. Despite this report, the use of intracameral antibiotics has not been universally adopted. METHODS: Various endophthalmitis prophylaxis patterns around the world (including the United States, Canada, Australia/New Zealand, Japan, China, India, Indonesia, South Africa, Argentina, Russia, Sweden and Mexico) are compared. Each contributing author was asked to provide similar information, including endophthalmitis rates based on published studies, current practice patterns, and in some cases original survey data. Various methods were used to obtain this information, including literature reviews, expert commentary, and some new survey data not previously published. RESULTS: Many different practice patterns were reported from around the world, specifically with respect to the use of intracameral antibiotics. CONCLUSION: There is no worldwide consensus regarding endophthalmitis prophylaxis with cataract 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".