Quality of Life after Pars Plana Vitrectomy, Scleral Buckle, or Pneumatic Retinopexy for Rhegmatogenous Retinal Detachment: A Meta-Analysis
Bibliographic record
Abstract
PURPOSE: Comparisons of the surgical and anatomic results of rhegmatogenous retinal detachment surgery have been investigated previously. A systematic evaluation of the available evidence comparing quality of life outcomes of either pars plana vitrectomy, scleral buckling, or pneumatic retinopexy has not been evaluated to date. This article analyzes whether pars plana vitrectomy, scleral buckling, or pneumatic retinopexy for the treatment of rhegmatogenous retinal detachment results in differing quality of life outcomes. METHODS: In February of 2022, a comprehensive search of MEDLINE, EMBASE, CINHAL, and Cochrane Library was conducted for studies on patients treated surgically for rhegmatogenous retinal detachment and included follow-up measurements of quality of life outcomes. Meta-analysis was completed using STATA v. 14.0. The main outcomes of interest were the mean vision-related quality of life score (VRQOL) and SD of VRQOL of each type of surgical procedure. RESULTS: = 1063), a better correlation was found between higher quality of life outcomes with scleral buckling than with pars plana vitrectomy (SMD = 0.62, CI: [0.31, 0.93]). There was also no signficant difference in quality of life outcomes between pneumatic retinopexy and pars plana vitrectomy (SMD = 0.08, CI: [-0.07, 0.22]). CONCLUSIONS: Scleral buckling results in better quality of life outcomes for patients when compared to pars plana vitrectomy. Pneumatic retinopexy did not show a difference in quality of life outcomes compared to pars plana vitrectomy.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.052 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".