Carbon Footprint of Pars Plana Vitrectomy, Pneumatic Retinopexy, and Scleral Buckle Procedures for Rhegmatogenous Retinal Detachment Repair
Bibliographic record
Abstract
Purpose: To quantify the environmental impact of pars plana vitrectomy (PPV), pneumatic retinopexy, and scleral buckle procedures used in rhegmatogenous retinal detachment (RRD) repair. Methods: We conducted a life cycle assessment of PPV, pneumatic retinopexy, and scleral buckle procedures. The primary outcome measure was the greenhouse gas emissions associated with each procedure measured in carbon dioxide equivalents. Results: The total greenhouse gas emissions produced were 51.10 kg carbon dioxide equivalents for PPV, 2.09 kg carbon dioxide equivalents for pneumatic retinopexy, and 12.57 kg carbon dioxide equivalents for scleral buckle. Emissions related to equipment use (30.07 kg carbon dioxide equivalents) followed by equipment manufacturing (21.00 kg carbon dioxide equivalents) were the main contributors of greenhouse gases in PPV. Emissions related to equipment manufacturing (1.60 kg and 8.51 kg of carbon dioxide equivalents for pneumatic retinopexy and scleral buckle, respectively), followed by equipment use (0.49 kg and 4.05 kg of carbon dioxide equivalents for pneumatic retinopexy and scleral buckle, respectively), were the greatest contributors of greenhouse gases in pneumatic retinopexy and scleral buckle. Conclusions: There is a substantial difference in greenhouse gas emissions among PPV, pneumatic retinopexy, and scleral buckle. Quantifying and understanding these differences can inform surgical decision-making.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".