Palliative care integration into outpatient heart failure management: pilot study
Bibliographic record
Abstract
OBJECTIVES: People with heart failure have palliative care needs yet services remain underused. The heart failure clinic is a potential setting for initial palliative care delivery though evidence for such services is lacking. We explored the outcomes of an embedded model of palliative medicine within a heart failure clinic. METHODS: We conducted a retrospective cohort study of individuals who received a palliative medicine consultation in a heart failure clinic. Descriptive statistics were used to characterise the cohort and their outcomes, and the McNemar test to compare rates of advance care planning before/after consultation. RESULTS: Majority of individuals who received a palliative medicine consultation experienced New York Heart Association (NYHA) class II symptoms (65.5%) and had a Palliative Performance Scale score of≥60% (66.8%). While only 17% engaged in advance care planning in the year before consultation, 93% had advance care planning during the first consultation (p<0.001). Care was provided in multiple domains including advance care planning (95%), symptom management (97%) and caregiver support (30%), regardless of the reason for referral. CONCLUSIONS: Our embedded model of palliative medicine within the heart failure clinic was associated with increased advance care planning at a time when patients were functional and minimally symptomatic. Further research should substantiate these findings at other sites.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 0.001 |
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".