Evaluation of intravitreal injections as a risk factor for capsular rupture during cataract surgery
Bibliographic record
Abstract
OBJECTIVE: To determine whether previous intravitreal injections are an independent risk factor for posterior capsular rupture (PCR) during cataract surgery after adjusting for known risk factors. DESIGN: Single-centre medical records analysis of a population-based cohort at a university-based referral centre. A retrospective cohort study has been conducted with inclusion of cataract surgeries done from January 1, 2005 to December 31, 2020 at the Department of Ophthalmology, Medical University of Graz, Austria. PARTICIPANTS: All consecutive cataract surgeries done in patients of at least 18 years of age from January 1, 2005 to December 31, 2020 have been included. METHODS: Association between previous intravitreal injections and PCR rates has been analysed through univariable and multivariable generalized estimating equations (GEE). Other investigated risk factors were age, combined surgery, pseudoexfoliation, surgeon's experience, and type of cataract surgery. RESULTS: A statistically significant higher rate of posterior capsular rupture during cataract surgery has been found in patients with previous intravitreal therapy compared with patients with no history of intravitreal therapy (OR 1.27, 95% CI 1.10-1.46, p = 0.008). However, after adjusting for confounding risk factors, no statistically significant effect was seen (OR 1.04, 95% CI 0.89-1.21, p = 0.664). CONCLUSION: We found no association between history of intravitreal injections and PCR during cataract surgery after adjusting for known risk factors. Further studies upon interactions between history of intravitreal injections and known risk factors for PCR, especially pseudoexfoliation, are needed.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".