Bibliographic record
Abstract
Introduction: Efficient planning is crucial for the safe evacuation of dialysis patients during a disaster. The lack of evidence-based approaches for evacuating these patients highlights the need to explore the associated challenges and develop a comprehensive plan to address the unique vulnerabilities of this cohort. Methods: Information was gathered using three methods: First, a thorough literature search was conducted. Secondly, a focus group was established, comprising experts in nephrology, biomedical engineering and safety engineering, as well as senior dialysis nurses. Finally, the research team visited a dialysis centre to examine the dialysis machines and engage in discussions regarding evacuation plans. Results: Three procedures were identified to promptly release patients from a dialysis machine: the ‘clamp and cut’ method, the ‘clamp and cap’ method, and the hand crank method. Factors such as the size and weight of the dialysis machine, battery life, and potential blood loss resulting from immediate interruption of the dialysis process were noted as important considerations. Conclusion: It is essential that dialysis patients be recognized as a vulnerable group, and that time and effort be invested in the design of an evacuation plan specific to their needs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".