OUTCOMES IN PRIMARY UNCOMPLICATED RHEGMATOGENOUS RETINAL DETACHMENT REPAIR USING PARS PLANA VITRECTOMY WITH OR WITHOUT SCLERAL BUCKLE
Bibliographic record
Abstract
PURPOSE: To compare outcomes after primary uncomplicated rhegmatogenous retinal detachment repair using pars plana vitrectomy (PPV) or PPV with scleral buckle (PPV-SB). METHODS: This is a retrospective cohort study with propensity score analysis in a single tertiary care center between 2014 and 2018 comparing patients with primary uncomplicated rhegmatogenous retinal detachment repaired using PPV only or PPV-SB (full cohort: n = 1,516, propensity-matched cohort: n = 908). The primary outcome was single surgery anatomic success, whereas secondary outcomes were 3-month and final pinhole visual acuity in logarithm of the minimum angle of resolution and final retina status. RESULTS: In the full cohort, single surgery anatomic success was achieved in 745 (91%) PPV patients versus 623 (89%) PPV-SB patients (P = 0.13). This was 390 (92%) versus 314 (88%) in phakic patients (P = 0.06) compared with 353 (91%) versus 301 (90%) in pseudophakic patients (P = 0.79), respectively. After matching, single surgery anatomic success was achieved in 424 (93%) PPV patients versus 412 (91%) PPV-SB patients (P = 0.14). Median pinhole visual acuity after PPV was better at 3 months (PPV: 20/40 vs. PPV-SB: 20/50; both cohorts: P < 0.001) and final follow-up (PPV: 20/29 vs. PPV-SB: 20/38; full cohort: P < 0.001 and PPV: 20/29 vs. PPV-SB: 20/36; matched cohort: P < 0.001). CONCLUSION: Addition of scleral buckle does not significantly change the rate of single surgery anatomic success compared with PPV only in primary uncomplicated rhegmatogenous retinal detachment. It is also associated with worse pinhole visual acuity at follow-up.
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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 | 0.004 |
| 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 | 0.001 |
| 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".