‘Climate Change and Health Indicators’ and ‘Surgical System Strengthening’: an opportunity for synergy
Bibliographic record
Abstract
Climate change is a public health emergency. Yet, incongruously, the healthcare sector is a significant source of global greenhouse gas emissions. Surgical systems are uniquely situated to address this public health crisis due to the high carbon footprint associated with surgical care delivery and the ability of strong surgical systems to foster broader climate resilience. There is an urgent need for climate change and health indicators (CCHIs) specific to surgical care to address the environmental impacts of surgery and reduce the impacts of climate change on the health of individuals, populations and surgical care delivery. In proposing a set of example CCHIs pertinent to surgical care, we call the surgical community to action to improve and refine existing indicators while simultaneously engaging in national and regional surgical system strengthening efforts. Moreover, aligning such efforts can help bridge existing climate and health funding gaps which, to date, have proved a critical barrier to ensuring effective healthcare mitigation and adaptation.
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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 | 0.066 | 0.080 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.020 | 0.030 |
| Open science | 0.003 | 0.028 |
| Research integrity | 0.012 | 0.023 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".