Direct Carbon Dioxide Emissions in Robotic Lung Resection–Analysis of a Prospective Cohort
Bibliographic record
Abstract
INTRODUCTION: Climate change is one of the most significant global health threats of the 21st century. Environmental concerns have led to increasing interest in reducing the carbon footprint of surgical procedures. However, there is a paucity of data evaluating direct carbon dioxide (CO2) emissions associated with robotic lung resection. This study aims to quantify direct CO2 emissions in robotic lung surgery. MATERIALS AND METHODS: Patients were identified from a prospectively maintained robotic thoracic database. Those undergoing either robotic segmentectomy or lobectomy for suspected or confirmed lung cancer from December 2017 to December 2022 were included. CO2 volume (liters; L) was used to calculate the weight (grams; g) of CO2 delivered into the chest cavity. Descriptive statistics were used to characterize CO2 utilization and linear regression was performed to identify factors associated with increased CO2 utilization. RESULTS: Of 528 patients identified, 421 had documented CO2 insufflation data and 90.97% (383/421) underwent either robotic segmentectomy or lobectomy. The mean weight of CO2 utilized was 756.52 (±409.86) g per patient and the weight of CO2 for the entire cohort was 0.29 metric tonnes. This is equivalent to a one-way flight from Toronto, Canada to Salt Lake City, USA. Segmentectomy, increased BMI, increased nodal harvest, and larger tumors were associated with reduced CO2 utilization. CONCLUSIONS: Direct CO2 emissions in robotic lung resection may be lower than previously assumed. While strategies to reduce CO2 utilization ought to be pursued, future studies should be aimed at establishing the optimal approach to mitigating the environmental impact of robotic lung resection.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".