Supplementary Material for: Intravitreal injection is associated with increased posterior capsule rupture risk during cataract surgery: A meta-analysis
Bibliographic record
Abstract
Abstract Background: Although observational studies have suggested that prior intravitreal therapy may predict posterior capsule rupture (PCR) during cataract surgery, this finding is still controversial. Objective: To summarize current evidence on the association between prior intravitreal injection (IVI) and PCR during cataract surgery. Methods: A systematic literature search was performed up to October 27th, 2021. Pooled odds ratios (ORs) and 95% confidence intervals (CIs) were calculated using random-effects models. The potential association between IVI and PCR in future cataract surgeries was assessed using the following two models: “pooling the odds ratios of PCR in eyes with and without previous IVI(s)” and “pooling the odds ratios for PCR relative to each increase in the number of prior injections.” The quality of included studies was appraised using the Newcastle-Ottawa Scale. Results: 6 cohort studies were included in this meta-analysis, with a total of 1,051,097 eyes that underwent cataract surgery. Of these, 7,034 eyes were associated with previous IVI. The pooled odds of PCR in eyes with prior IVI was 2.01 (95% CI: 1.35–3.00) times higher than that of eyes without an IVI history. An increase in the number of previous IVI conferred increased odds of PCR of 1.03 (95% CI: 1.01–1.06). After excluding studies that failed to account for confounders, the significantly increased risk was not altered, and the significant heterogeneity was minimized in both models. Conclusion: This meta-analysis provides evidence that previous IVI significantly increases the risk of PCR during future cataract surgery. The risk of PCR should be discussed preoperatively with patients. Further studies are required to validate our findings and explore the underlying mechanisms.
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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 | 0.004 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.502 | 0.023 |
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".