Dialysis Patients’ Preferences on Resuscitation: A Cross-Sectional Study Design
Bibliographic record
Abstract
Background: End-stage kidney disease is associated with a 10- to 100-fold increase in cardiovascular mortality compared with age-, sex-, and race-matched population. Cardiopulmonary resuscitation (CPR) in this cohort has poor outcomes and leads to increased functional morbidity. Objective: The aim of this study is to assess patients' preferences toward CPR and advance care planning (ACP). Design: cross-sectional study design. Setting: Two outpatient dialysis units. Patients: Adults undergoing dialysis for more than 3 months were included. Exclusion criteria were severe cognitive impairment or non-English-speaking patients. Measurements: A structured interview with the use of Willingness to Accept Life-Sustaining Treatment (WALT) tool. Methods: test were performed along with probability plot for testing hypotheses. Results: Seventy participants were included in this analysis representing a 62.5% response rate. There was a clear association between treatment burden, anticipated clinical outcome, and the likelihood of that outcome with patient preferences. Low-burden treatment with expected return to baseline was associated with 98.5% willingness to accept treatment, whereas high-burden treatment with expected return to baseline was associated with 94.2% willingness. When the outcome was severe functional or cognitive impairment, then 45.7% and 28.5% would accept low-burden treatment, respectively. The response changed based on the likelihood of the outcome. In terms of resuscitation, more than 75% of the participants would be in favor of receiving CPR and mechanical ventilation at their current health state. Over 94% of patients stated they had never discussed ACP, whereas 59.4% expressed their wish to discuss this with their primary nephrologist. Limitations: Limited generalizability due to lack of diversity. Unclear decision stability due to changes in health status and patients' priorities. Conclusions: ACP should be incorporated in managing chronic kidney disease (CKD) to improve communication and encourage patient involvement.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".