Our shared responsibility: the urgent necessity of global environmentally sustainable kidney care
Bibliographic record
Abstract
In response to Earth's accelerating climate crisis, we, an international group of nephrologists, call on our global community to unite and align kidney care in accordance with United Nation's 26th Conference of the Parties health sector principles. We announce a global and inclusive initiative, "GREEN-K": Global Environmental Evolution in Nephrology and Kidney Care, with a vision of "sustainable kidney care for a healthy planet and healthy kidneys" and mission to "promote and support environmentally sustainable and resilient kidney care globally through advocacy, education, and collaboration." A patient-centric approach that permits climate change mitigation and adaptation is proposed. Multi-stakeholder GREEN-K action and focus areas will include education, sustainable clinical care, and advances toward environmentally sustainable innovations, procurement, and infrastructure. Mindful of the disproportionately high climate impact of kidney therapies, we welcome the opportunity to work together in shared accountability to patients and Earth's natural systems.
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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 | 0.056 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.028 |
| Scholarly communication | 0.026 | 0.034 |
| Open science | 0.005 | 0.037 |
| Research integrity | 0.020 | 0.042 |
| Insufficient payload (model declined to judge) | 0.022 | 0.007 |
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".