Incidence of endophthalmitis after phacoemulsification cataract surgery: a Meta-analysis
Bibliographic record
Abstract
AIM: To evaluate the overall endophthalmitis incidence and the effectiveness of potential prophylaxis measures following phacoemulsification cataract surgery (PCS). METHODS: , 2021. We included studies that reported on the incidence of endophthalmitis following PCS. The quality of the included studies was critically evaluated with the Newcastle-Ottawa quality assessment scale. The random effect or the fixed-effects model was used to evaluated the pooled incidence based on the heterogeneity. The publication bias was assessed by Egger's linear regression and Begg's rank correlation tests. RESULTS: A total of 39 studies containing 5 878 114 eyes were included and critically appraised in the Meta-analysis. For overall incidence of endophthalmitis after PCS, the Meta-analysis yielded a pooled estimate of 0.092% (95%CI: 0.083%-0.101%). The incidence appeared to decrease with time (before 2000: 0.097%, 95%CI: 0.060%-0.135%; 2000 to 2010: 0.089%, 95%CI: 0.076%-0.101%; after 2010: 0.063%, 95%CI: 0.050%-0.077%). Compared with typical povidone-iodine solution (0.178%, 95%CI: 0.071%-0.285%) and antibiotics subconjunctival injections (0.047%, 95%CI: 0.001%-0.095%), the use of intracameral antibiotics significantly reduced the incidence of endophthalmitis after PCS (0.045%, 95%CI: 0.034%-0.055%, RR: 7.942, 95%CI: 4.510-13.985). CONCLUSION: Due to the advancement of phacoemulsification technology and the widespread use of intracameral antibiotics, the incidence of endophthalmitis following PCS shows a decreasing trend over time. The use of intracameral antibiotics administration will significantly reduce the risk of endophthalmitis.
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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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.063 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".