Outcomes of Combined Scleral Buckling Plus Pneumatic Retinopexy Vs. Scleral Buckling for Primary Rhegmatogenous Retinal Detachment
Bibliographic record
Abstract
Purpose To evaluate the outcomes and complications of scleral buckle surgery alone or combined with pneumatic retinopexy (pneumatic buckle) for the treatment of primary rhegmatogenous retinal detachment. Design Retrospective chart review. Participants Two hundred thirteen patients with rhegmatogenous retinal detachment of whom 101 underwent primary scleral buckle surgery at Rabin Medical Center in 2005–2015 (SB group) and 112 underwent pneumatic buckle surgery at Royal Alexandra Hospital in 2013–2015 (PB group). Methods All patients were followed for ≥12 months. Data on clinical and surgical parameters, outcome, and complications were collected from the medical files. Main Outcome Measures Best corrected visual acuity and anatomical outcomes. Results At 12 months, average best corrected visual acuity was 0.3 logMar in the SB group and 0.42 logMar in the PB group ( P < 0.05). Rates of anatomical reattachment were high and similar in the two groups (99% and 97%, respectively, P = 0.623). The SB group had a higher percentage of patients requiring additional laser applications (21% vs. 7%; P < 0.01) and buckle readjustment surgery (6% vs. 0; P = 0.01), and the PB group had a higher percentage of patients who required postoperative pars plana vitrectomy (30% vs. 17%; P = 0.03). Conclusion Scleral buckle surgery alone is efficient for the treatment of rhegmatogenous retinal detachment. Its combination with pneumatic retinopexy usually has no significant added value in terms of anatomical reattachment rate. Outcomes of Pneumatic buckling vs Scleral Buckling for RRD
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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 | 0.001 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".