A Roadmap for Disaster Risk Reduction and Management in Kidney Care
Bibliographic record
Abstract
Key Points Disasters cause significant human suffering, and patients with kidney diseases are uniquely vulnerable. We have developed a roadmap for disaster preparedness, response, and recovery by reviewing and synthesizing existing literature. Our roadmap provides an easily implementable approach for kidney care programs to develop context-specific protocols. Background Natural, technological, and other disasters cause significant human suffering, and kidney patients are uniquely vulnerable. The safe provision of KRTs necessitates the consistent provision of resources. Robust disaster risk reduction and management (DRRM) can mitigate risks associated with resource disruption. Individual kidney care programs may benefit from an organized approach to developing context-specific protocols. We aimed to synthesize contemporary literature in kidney care to create a roadmap in DRRM. Methods We conducted a scoping review followed by a content analysis using the Framework Method. Literature that focused on lessons learned and proposed strategies or recommendations in DRRM was eligible. We contextualized this roadmap within the domains of disaster preparedness, response, and recovery. Results Of 3973 titles and abstracts screened, 52 articles were included. We developed the following roadmap: ( 1 ) the “ABC 4 s” of disaster preparedness: assess needs, risks, and vulnerabilities (regional risks and patients at risk); build a task force network; capacity building (tangible resources, intangible resources, monetary considerations, and transportation); communication (network and protocol, patients' medical and dialysis information, contact information of all stakeholders, inclusive approach, and reliable medium); coaching (patients, caregivers, health care personnel, and reinforce and repeat); contingency planning (surge capacity, rationing care, and resource distribution); and strategic partnerships. ( 2 ) The DIAL response: damage and scope assessment; initiate action plan (choose the plan, apply preparedness tenets, and implications for receiving facilities); appraise the action plan regularly (reassess, maintain ethical standards, and address psychosocial needs); and liaise, engage, and update. ( 3 ) The ARC to recovery: assess damage; return to the (new) norm; and collect data to evaluate, improve, and share. Conclusions We propose a roadmap to disaster preparedness, response, and recovery that can guide individual kidney care programs globally to develop context-specific protocols aimed at building capacities and facilitating processes toward DRRM. Podcast This article contains a podcast at https://dts.podtrac.com/redirect.mp3/www.asn-online.org/media/podcast/JASN/2025_06_25_ASN0000000635.mp3
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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 | 0.082 | 0.098 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.026 | 0.018 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.018 | 0.027 |
| Open science | 0.008 | 0.018 |
| Research integrity | 0.015 | 0.014 |
| Insufficient payload (model declined to judge) | 0.025 | 0.008 |
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".