Post-Cataract Surgery Endophthalmitis Prophylaxis Survey in Thai Ophthalmologists
Bibliographic record
Abstract
Purpose: To determine current perioperative practice patterns for the prevention of endophthalmitis following cataract surgery in Thailand. Design: Cross-sectional Survey Study. Methods: An online questionnaire was distributed nationwide to certified ophthalmologists, consisting of 21 questions covering 10 preoperative, 7 intraoperative, and 4 postoperative measures for the prevention of post-cataract surgery endophthalmitis. Subgroup analyses were performed to compare practices between general ophthalmologists, retina/uveitis specialists (R/U), and other subspecialists (Non-R/U). Results: A total of 426 responses were analyzed. Preoperative topical antibiotics (21.1%, n = 90) and oral antibiotics (4.9%, n = 21) were rarely prescribed. The most common antiseptic for skin preparation was 10% povidone-iodine (72.3%, n = 308). Intracameral antibiotics were adopted by 54.7% of respondents (n = 233). Postoperative topical antibiotics were almost universally prescribed (99.5%, n = 424). Moxifloxacin was the most preferred topical (preoperative: 45.6%, n = 41, postoperative: 46.4%, n = 197) and intracameral (57.1%, n = 133) antibiotic. Subspecialty analysis revealed that R/U specialists were more prone to use 10% povidone-iodine (relative risk ratio [RRR] 2.37, 95% CI 1.16 to 4.82) and antiseptic applied to fornix (RRR 2.62, 95% CI 1.26 to 5.46) than the general ophthalmologist group. Conclusion: Although the majority of Thai ophthalmologists align their practices with international endophthalmitis prevention guidelines, certain practices were less frequently adopted than recommended. These findings emphasize the need for standardized national practice patterns to enhance surgical outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 teacher head, 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".