AN APPRAISAL OF END‐OF‐LIFE CARE IN PERSONS WITH CHRONIC KIDNEY DISEASE DYING IN HOSPITAL WARDS
Bibliographic record
Abstract
AIM: To review end-of-life care provided by renal healthcare professionals to hospital in-patients with chronic kidney disease, and their carers, over a 12-month period in Northern Ireland. METHODS: Retrospective review of 100 patients. RESULTS: Mean age at death was 72 years (19-95) and 56% were male. Eighty three percent of patients had a 'Not For Attempted Resuscitation' order during their last admission and this was implemented in 42%. Less than 20% of all patients died in a hospital ward. No patients had an advanced care plan, although 42% had commenced the Liverpool Care Pathway for the Dying Patient. Patients suffered excessive end-of-life symptoms. In addition, there was limited documentation of carer involvement and carer needs were not formally assessed. CONCLUSION: End-of-life care for patients with advanced chronic renal disease can be enhanced. There is significant variation in the recording of discussions regarding impending death and little preparation. There is poor recording of the patients' wishes regarding death. Those with declining functional status, including those frequently admitted to hospital require holistic assessment regarding end-of-life needs. More effective communication between the patient, family and multi-professional team is required for patients who are dying and those caring for them.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".