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Record W4413957421 · doi:10.1177/24741264251367120

Carbon Footprint of Pars Plana Vitrectomy, Pneumatic Retinopexy, and Scleral Buckle Procedures for Rhegmatogenous Retinal Detachment Repair

2025· article· en· W4413957421 on OpenAlexaff
Rahul Moorjani, Graeme K. Loh, Matthew Tennant, Peter J. Kertes, Parampal S. Grewal

Bibliographic record

VenueJournal of VitreoRetinal Diseases · 2025
Typearticle
Languageen
FieldMedicine
TopicRetinal and Macular Surgery
Canadian institutionsUniversity of TorontoUniversity of Alberta
Fundersnot available
KeywordsPars planaScleral buckleVitrectomyRetinal detachmentOphthalmologyScleral bucklingMedicineRetinalVisual acuity

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.903

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.275
Teacher spread0.267 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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