Time to integrate climate science into kidney care planning: a ‘PASIGE’ to a climate change mitigation and adaptation framework
Bibliographic record
Abstract
PURPOSE OF REVIEW: Kidney diseases affect around 850 million people globally and are a growing public health burden, with high rates of associated cardiovascular mortality and no major decline in age-standardized mortality compared to other noncommunicable diseases. Climate change is an inequitable driver of kidney diseases, and climate-related disasters can disrupt access to life-sustaining kidney replacement therapies. Conversely, the care of patients with kidney diseases contributes to greenhouse gas emissions, pollution, and generates large amounts of waste. RECENT FINDINGS: Environmentally sustainable kidney care planning is pursuing kidney care practices and innovations that minimize environmental harm while remaining patient-centered and cost-effective. An adaptation and mitigation framework (a structured approach to developing adaptation strategies, policies, and measures) to guide this is lacking. SUMMARY: We propose the 'PASIGE' framework to guide climate science integration in kidney care planning -> Prevent: approaches to prevent kidney disease, its progression to kidney failure, and complications; Adopt: sustainable lifestyle, practices and therapies; Screen: targeted population screening for early detection and identification of kidney disease; Innovate: technology, manufacturing, procurement, energy sources and transportation; Generate: sustainably powered and produced low-impact net zero waste kidney replacement therapies resilient to climate threats; and Enhance: patient engagement, care quality, and system resiliency.
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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 | 0.026 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".