Supporting the Heterogeneous and Evolving Treatment Preferences of Patients With Heart Failure Through Collaborative Home‐Based Palliative Care
Bibliographic record
Abstract
Background We characterized the treatment preferences, care setting, and end-of-life outcomes among patients with advanced heart failure supported by a collaborative home-based model of palliative care. Methods and results This decedent cohort study included 250 patients with advanced heart failure who received collaborative home-based palliative care for a median duration of 1.9 months of follow-up in Ontario, Canada, from April 2013 to July 2019. Patients were categorized into 1 of 4 groups according to their initial treatment preferences. Outcomes included location of death (out of hospital versus in hospital), changes in treatment preferences, and health service use. Among patients who initially prioritized quantity of life, 21 of 43 (48.8%) changed their treatment preferences during follow-up (mean 0.28 changes per month). The majority of these patients changed their preferences to avoid hospitalization and focus on comfort at home (19 of 24 changes, 79%). A total of 207 of 250 (82.8%) patients experienced an out-of-hospital death. Patients who initially prioritized quantity of life had decreased odds of out-of-hospital death (versus in-hospital death; adjusted odds ratio, 0.259 [95% CI, 0.097-0.693]) and more frequent hospitalizations (mean 0.45 hospitalizations per person-month) compared with patients who initially prioritized quality of life at home. Conclusions Our results yield a more detailed understanding of the interaction of advanced care planning and patient preferences. Shared decision making for personalized treatment is dynamic and can be enacted earlier than at the very end of life.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".