End‐of‐life care decisions for haemodialysis patients – ‘We only tend to have that discussion with them when they start deteriorating’
Bibliographic record
Abstract
BACKGROUND: Haemodialysis patients receive very little involvement in their end-of-life care decisions. Issues relating to death and dying are commonly avoided until late in their illness. This study aimed to explore the experiences and perceptions of doctors and nurses in nephrology for involving haemodialysis patients in end-of-life care decisions. METHODS: A semi-structured qualitative interview study with 15 doctors and five nurses and thematic analysis of their accounts was conducted. The setting was a large teaching hospital in Wales, UK. RESULTS: Prognosis is not routinely discussed with patients, in part due to a difficulty in estimation and the belief that patients do not want or need this information. Advance care planning is rarely carried out, and end-of-life care discussions are seldom initiated prior to patient deterioration. There is variability in end-of-life practices amongst nephrologists; some patients are felt to be withdrawn from dialysis too late. Furthermore, the possibility and implications of withdrawal are not commonly discussed with well patients. Critical barriers hindering better end-of-life care involvement for these patients are outlined. CONCLUSIONS: The study provides insights into the complexity of end-of-life conversations and the barriers to achieving better end-of-life communication practices. The results identify opportunities for improving the lives and deaths of haemodialysis patients.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".