Reattachment rate with pneumatic retinopexy versus pars plana vitrectomy for single break rhegmatogenous retinal detachment
Bibliographic record
Abstract
AIM: To assess the primary reattachment rate (PARR) in pneumatic retinopexy (PnR) versus pars plana vitrectomy (PPV) for rhegmatogenous retinal detachment (RRD) meeting the Pneumatic Retinopexy versus Vitrectomy for the Management of Primary Rhegmatogenous Retinal Detachment Outcomes Randomised Trial (PIVOT) criteria with a single break in detached retina. METHODS: A post hoc analysis of two clinical trials. To be included, patients with primary RRD had to meet PIVOT criteria but could have only one break in the detached retina. Patients with additional pathology in the attached retina were included in a secondary analysis. The primary outcome was PARR following PnR versus PPV at 1-year postoperatively. RESULTS: 162 patients were included. 53% (86/162) underwent PnR and 47% (76/162) had a PPV. 99% (85/86) and 86.8% (66/76) completed the 1-year follow-up visits in the PnR and PPV groups, respectively. PARR was 88.2% (75/85) in the PnR group and 90.9% (60/66) in the PPV group (p=0.6) with a mean postoperative logMAR best-corrected visual acuity of 0.19±0.25 versus 0.34±0.37 (Snellen 20/30 vs 20/44) (p=0.01) each in the PnR and PPV groups, respectively.In an additional analysis of patients who were also allowed to have any pathology in the attached retina, the PARR was 85% (91/107) and 91.6% (66/72) in the PnR and PPV groups, respectively (p=0.18). CONCLUSIONS: PnR and PPV provide similar long-term PARR in a substantial proportion of patients meeting PIVOT criteria with only a single break in the detached retina. Therefore, in patients meeting these specific criteria, PnR is an appropriate first-line therapy as it offers superior functional outcomes without compromising PARR.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".