Anterior vitrectomy incidence in cataract surgery among experienced surgeons and residents: A systematic review and meta-analysis
Bibliographic record
Abstract
PurposeCataract surgery is a fundamental procedure in ophthalmology, yet intraoperative complications such as anterior vitrectomy can compromise surgical outcomes. This systematic review and meta-analysis (CRD42025637001) aim to compare the incidence of anterior vitrectomy in cataract surgeries performed by ophthalmology residents versus experienced surgeons and assess factors contributing to surgical complications.MethodsA systematic search was conducted across EMBASE, MEDLINE, CINAHL Plus, Web of Science, ClinicalTrials.gov, PQDT Global, ARVO and AAO for studies published after 2000 that reported on anterior vitrectomy incidence in cataract surgery. Eligible studies included randomized controlled trials and observational studies. Meta-analysis was performed using STATA v. 18.0.ResultsOut of 1,190 screened studies, five studies (four retrospective cohort, one prospective cohort) involving phacoemulsification, extracapsular cataract extraction (ECCE), and femtosecond laser-assisted cataract surgery (FLACS) were included, encompassing a total of 4,918 cataract surgeries, and 208 anterior vitrectomy (AV) cases. The random-effects meta-analysis demonstrated a significant AV incidence for residents (ES = 0.04, 95% CI: [0.01, 0.06]), while the incidence for experienced surgeons was not statistically significant (ES = 0.03, 95% CI: [-0.03, 0.09]). High heterogeneity was observed among the included studies (I² = 92.1% for residents and I² = 96.7% for surgeons).ConclusionResidents may have a higher incidence of AV, highlighting the potential benefits of structured surgical training, early exposure, and mentorship in reducing intraoperative complications. Future research should explore simulation-based training and technology-assisted surgery to improve resident proficiency and patient outcomes.
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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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.046 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".