Diabetes, Diabetic Retinopathy, and Intravitreal Injection as a Risk Factor of Posterior Capsular Rupture in Phacoemulsification Cataract Surgery :A Systematic Review and Meta Analysis
Bibliographic record
Abstract
Abstract Introduction & Objectives : The risk of PCR in patients with diabetes and its sequential complication including diabetic retinopathy and Intravitreal Injection (IVI) was still under debate. This study was aimed to summarize the risk of diabetes, diabetic retinopathy, and intravitreal injection towards PCR events during phacoemulsification cataract surgery. Methods : A systematic literature search was performed up to March 2023 from the last 10 years publications using Medline and Cochrane library databases. Pooled odds ratios (ORs) and 95% confidence intervals (CIs) of diabetes, diabetic retinopathy, and IVI with PCR events were calculated using random-effects models. The quality of included studies was appraised using the Newcastle-Ottawa Scale. Results : Six studies about diabetes, six studies about diabetic retinopathy and seven studies about IVI were included in this meta-analysis. The pooled odds ratios of PCR in eyes with diabetes was 1.22 (95% CI: 1.16-1.28) I2 =0.0%, with the diabetic retinopathy patients was 1.23 (95% CI: 1.13-1.33) I2 =64.3%, and with prior IVI was 1.05 (95% CI: 1.03-1.08) I2 =51%. Conclusion : This meta-analysis emphasize that diabetes, diabetic retinopathy, and previous IVI significantly increase the risk of PCR on future phacoemulsification surgery. Further studies are needed to elucidate the underlying mechanism.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.039 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| 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".