Nephrologists’ Reported Preparedness for End-of-Life Decision-Making
Bibliographic record
Abstract
Nephrologists commonly engage in end-of-life decision-making with patients with ESRD and their families. The purpose of this study was to determine the perceived preparedness of nephrologists to make end-of-life decisions and to determine factors that are associated with the highest level of perceived preparedness. The nephrologist members of the Renal Physicians Association (RPA) and the Canadian Society of Nephrology were invited to participate in an online survey of their end-of-life decision-making practices. A total of 39% of 360 respondents perceived themselves as very well prepared to make end-of-life decisions. Age >46 yr, six or more patients withdrawn from dialysis in the preceding year, and awareness of the RPA/American Society of Nephrology (ASN) guideline on dialysis decision-making were independently associated with the highest level of self-reported preparedness. Nephrologists who reported being very well prepared were more likely to use time-limited trials of dialysis and stop dialysis of a patient with permanent and severe dementia. Compared with Americans, Canadian nephrologists reported being equally prepared to make end-of-life decisions, stopped dialysis of a higher number of patients, referred fewer to hospice, and were more likely to stop dialysis of a patient with severe dementia. Nephrologists who have been in practice longer and are knowledgeable of the RPA/ASN guideline report greater preparedness to make end-of-life decisions and report doing so more often in accordance with guideline recommendations. To improve nephrologists' comfort with end-of-life decision-making, fellowship programs should teach the recommendations in the RPA/ASN guideline and position statement.
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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 | 0.006 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".