PNEUMATIC RETINOPEXY FOR GIANT RETINAL TEAR–ASSOCIATED RETINAL DETACHMENT
Bibliographic record
Abstract
PURPOSE: To report the technique and long-term outcomes of patients with giant retinal tear-associated retinal detachment treated with pneumatic retinopexy (PnR). METHODS: Retrospective cohort study. All patients presenting with giant retinal tear-associated retinal detachment with tears in the superior ten-clock hours who underwent primary PnR were included in this study . RESULTS: Thirty-one patients were included in the study. Of these, 61.2% (19 of 31) achieved primary reattachment rate (PARR) with PnR at 3 months and 58.0% (18 of 31) at the final follow-up. Patients included in this study had a median follow-up of 24 months (interquartile range 46.5 months). The absence of retinal tears elsewhere at baseline was associated with a final PARR of 80% (16 of 20) ( P = 0.007). Thirteen eyes required pars plana vitrectomy after a failed PnR. Two eyes required the intraoperative use of perfluorocarbon liquids. No eyes required silicone oil. Visual acuity improved significantly from baseline to the last follow-up. Final anatomical reattachment rate was 100% (31 of 31). CONCLUSION: For selected cases of giant retinal tear associated retinal detachments affecting the superior ten-clock hours, PnR could be a possible treatment option when patients consent to extra visits and the surgeon has substantial expertise. When lacking this extensive experience and comfort with PnR, pars plana vitrectomy remains the treatment that is most likely to result in a primary anatomical reattachment. Although this study provides guidance on PnR technique for giant retinal tear associated retinal detachments, it is essential to note that the reported PARR may be contingent on the expertise of the surgeon/center, and the authors recommend that those new to PnR first gain substantial experience in cases meeting clinical trial criteria.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".