Implementation of pneumatic retinopexy in the Japanese population
Bibliographic record
Abstract
PURPOSE: To propose an implementation model for pneumatic retinopexy (PnR) in a region where PnR is performed infrequently, and to assess its impact on treatment of rhegmatogenous retinal detachment (RRD). STUDY DESIGN: Retrospective case series. METHODS: We reviewed 222 consecutive eyes with primary RRD treated from July 2017 to September 2023 at a tertiary care center in Japan. The treatment methods utilized included pars plana vitrectomy (PPV), scleral buckling (SB) and PnR. The surgeon learned PnR through social media. Primary anatomic reattachment rate (PARR) and visual acuity outcomes were compared between the pre-PnR (prior to the implementation; 110 eyes) and post-PnR (after the implementation; 112 eyes) periods, as well as between PnR and PPV in the post-PnR period. PARR for PnR was also evaluated based on RRD characteristics and gas injection frequency. RESULTS: In the post-PnR period PnR was performed in 53.6% (60/112)of cases. The PARR was similar in the pre-PnR (97.3%) and post-PnR (93.8%) periods (P=.33). Visual outcomes were similar both across periods and between PnR and PPV at 3, 6 and 12 month post-operatively. The PARR for PnR was 88.3% overall, 90.5% in eyes meeting the Primary Rhegmatogenous Retinal Detachment Outcomes Randomized Trial (PIVOT) criteria, 93.3% in eyes with a single break and 100% in eyes with a single break meeting PIVOT criteria. Eyes with a single gas injection had higher PARR than eyes requiring an additional gas injection (93.5% vs. 71.4%). CONCLUSION: Remote-learning utilizing social media effectively enabled PnR implementation with favorable anatomic and functional outcomes in a real-world setting in Japan.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".