Regional collaborative home-based palliative care and health care outcomes among adults with heart failure
Bibliographic record
Abstract
BACKGROUND: Innovative models of collaborative palliative care are urgently needed to meet gaps in end-of-life care among people with heart failure. We sought to determine whether regionally organized, collaborative, home-based palliative care that involves cardiologists, primary care providers and palliative care specialists, and that uses shared decision-making to promote goal- and need-concordant care for patients with heart failure, was associated with a greater likelihood of patients dying at home than in hospital. METHODS: We conducted a population-based matched cohort study of adults who died with chronic heart failure across 2 large health regions in Ontario, Canada, between 2013 and 2019. The primary outcome was location of death. Secondary outcomes included rates of health care use, including unplanned visits to the emergency department, hospital admissions, hospital lengths of stay, admissions to the intensive care unit, number of visits with primary care physicians or cardiologists, number of home visits by palliative care physicians or nurse practitioners, and number of days spent at home. RESULTS: = 1172) who received usual care, with 101 (41.2%) and 917 (78.2%) patients, respectively, dying in hospital (number needed to treat = 3). Additional associated benefits of the collaborative approach included higher rates of clinician home visits, longer time to first hospital admission, shorter hospital stays and more days spent at home. INTERPRETATION: Adoption of a model of regionally organized, collaborative, home-based palliative care that uses shared decision-making may improve end-of-life outcomes for people with chronic heart failure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".