Managing greenhouse gas emissions in the terminal year of life in an overwhelmed health system: a paradigm shift for people and our planet
Bibliographic record
Abstract
Health care contributes 4·4% of global net carbon emissions. Hospitals are resource-intensive settings, using a large amount of supplies in patient care and have high energy, ventilation, and heating needs. This Viewpoint investigates emissions related to health care in a patient's last year of life. End of life (EOL) is a period when health-care use and associated emissions production increases exponentially due primarily to hospital admissions, which are often at odds with patients' values and preferences. Potential solutions detailed within this Viewpoint are facilitating advanced care plans with patients to ensure their EOL wishes are clear, beginning palliative care interventions earlier when treating a life-limiting illness, deprescribing unnecessary medications because medications and their supply chains make up a significant portion of health-care emissions, and, enhancing access to low-intensity community care settings (eg, hospices) within the last year of life if home care is not available. Our analysis was done using Canadian data, but the findings can be applied to other high-income countries.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".