End-of-Life Care Among Patients With Kidney Failure on Maintenance Dialysis: A Retrospective Population-Based Study
Bibliographic record
Abstract
Background: Nephrologists routinely provide end-of-life care for patients with kidney failure (KF) on maintenance dialysis. Involvement of primary care and palliative care physicians may enhance this experience. Objective: The objective was to describe outpatient care patterns in the last year of life and the end-of-life acute care utilization for patients with KF on maintenance dialysis. Design: Retrospective cohort study using population-level health administrative data. Setting & Participants: Outpatient and inpatient care during the last year of life among patients who died between 2017 and 2019, receiving maintenance dialysis in Ontario, Canada. Measurements: The primary exposure is patterns of physician specialties providing outpatient care in the last year of life. Outcomes include outpatient encounters in the last year of life, acute care visitation in the last month of life, and place of death. Methods: < .05, two-tailed). Results: Among 6866 patients, the median age at death was 73, 36.1% were female, and 87.8% resided in urban regions. Three patterns emerged: a primary care, nephrology, and palliative care triad (25.5%); a primary care and nephrology dyad (59.3%); and a non-primary care pattern (15.2%). Palliative care involvement is concentrated near death. Of all, 81.4% spent at least 1 day in hospital or emergency department in the last month, but those with primary care, palliative care, and nephrology involvement had the fewest acute care deaths (65.8%). Limitations: Outpatient care patterns were defined using physician billing codes, potentially missing care from other providers. Conclusions: Nephrology and primary care predominantly manage outpatient care in the last year of life for patients with KF on maintenance dialysis, with consistent acute care use across care patterns except for the place of death. Future research should explore associations between patterns of care and end-of-life outcomes to identify the most optimal model of care for patients with KF on maintenance dialysis.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".