Scleral buckling as a standalone surgery in the era of pars plana vitrectomy: a 10-year retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Scleral buckling (SB) is an established treatment for rhegmatogenous retinal detachment (RRD). Once the gold standard for RRD repair, SB became less performed with the rise of pars plana vitrectomy (PPV). This retrospective interventional cohort study aims to provide 10 years of real-world data on SB surgical outcomes in a Canadian tertiary eye center. METHODS: Patients undergoing primary SB surgery at the CHU de Québec between January 2014 and December 2023 for primary RRD with at least 3 months of postoperative follow-up were included. Multiple linear and logistic regression models were developed to identify variables associated with final BCVA and single surgery anatomical success (SSAS). RESULTS: A total of 187 phakic patients undergoing primary SB surgery with a median [Q1, Q3] follow-up (FU) of 23.0 [11.0, 38.9] months were included. Females constituted 56.1% (n = 105) of the cohort with a median age at surgery of 36.9 [28.6, 47.0] years. Most patients did not have a preoperative posterior vitreous detachment (n = 161, 86.1%). Macula status was on for 109 (58.3%), off for 46 (24.6%), and split for 32 (17.1%) patients. Median RRD extent was of 4.0 [3.0, 5.0] clock hours, with the preoperative number of retinal breaks being of 2.0 [1.0, 3.0]. Inferior RRD associated with inferior breaks was present in 101 (54.0%) eyes. SSAS was achieved in 169 (90.4%) cases, with 184 (98.4%) patients achieving retinal reattachment at last follow-up. BCVA progressed from baseline median values of 0.14 [0.00, 0.40] logMAR, improving by 12 months (0.12 [0.02, 0.30], p = 0.004) and by final follow-up (0.10 [0.00, 0.28], p < 0.001). Male sex was a negative predictor of SSAS (p = 0.008), while SF6 gas tamponade was a positive predictor (p = 0.035). Chronicity of RRD (p = 0.012) and worse preoperative BCVA (p < 0.001) were associated with worse final BCVA. CONCLUSIONS: The use of primary SB as a standalone surgery continues to be a safe and effective technique for RRD repair, specifically in young phakic patients with no PVD; or when PPV may not be the preferred option to preserve lens state and avoid iatrogenic retinal breaks, two well-documented complications of 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".