Patterns of Health Care Delivery Among Adults With Heart Failure in the Last Year of Life: A Retrospective Population‐Based Study
Bibliographic record
Abstract
BACKGROUND: People with heart failure (HF) are treated by multiple physician specialties as they approach the end of life (EOL). Patterns of physician involvement and health outcomes are not well understood. Elucidation of care patterns for this population may identify opportunities to minimize fragmentation and improve EOL continuity. This study describes categories of outpatient physician care patterns in the last year of life for people with HF and how EOL acute care use varies by category. METHODS AND RESULTS: We conducted a retrospective cohort study of 65 625 adults with HF (median age, 83 [interquartile range, 74-89] years; 44.2% women; 86.9% urban residents) who died between 2017 and 2019 in Ontario, Canada, using health administrative data. Individuals were categorized according to different combinations of outpatient care providers in the last year of life: (1) primary care, palliative care, and relevant specialties (25.9%); (2) primary care and palliative care (5.4%); (3) primary care and relevant specialties (40.8%); (4) primary care (18.6%); and (5) specialty care (9.3%). Primary care physicians maintained involvement throughout the last year of life, while the proportion of monthly palliative care encounters increased near death. People who had palliative care involvement had the lowest rates of hospitalization and acute care deaths compared with those without palliative care involvement. CONCLUSIONS: People with HF receive most outpatient care from primary care physicians and palliative care physicians at the EOL. Multiple specialties are involved, highlighting the patients' medical complexity. Findings may help inform ways to measure relational continuity at the EOL for patients with HF.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| 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".