Comparison of Palliative Care Interventions for Cancer versus Heart Failure Patients: A Secondary Analysis of a Systematic Review
Bibliographic record
Abstract
Abstract Background: In 2016, Kavalieratos and colleagues performed a systematic review of randomized clinical trials (RCTs) of palliative care (PC) interventions. The majority of RCTs included focused on oncology, with fewer in heart failure (HF). Cancer patients' often predictable decline differs from the variable illness trajectories of HF; however, both groups experience similar palliative needs, and accordingly, PC in HF continues to grow. Objective: To investigate if PC interventions differ between cancer and HF patients. Design: In this secondary analysis, we compare PC interventions for cancer and HF patients evaluated in the 2016 systematic review. Settings/Subjects: We included a total of 25 trials, 19 of which included 3730 cancer patients, and 6 of which included 1049 HF patients (mean age, 67 years). Measurements: We compared the following five characteristics among included trials: PC domains addressed, duration, location, provider specialization, and measured outcomes. Results: The content of the cancer and HF interventions was similar. HF interventions tended to include more home-based (50% vs. 37%) and specialty PC interventions (67% vs. 47%), although these results did not reach statistical significance. Both cancer and HF interventions favored longer durations (i.e., more than one month; 79% and 67%). No HF intervention RCTs included caregiver outcomes, whereas 32% of cancer interventions did. Conclusions: There were no substantial differences in content of cancer and HF interventions, although the latter tended to be delivered by PC specialists at home. There is a need for scalable interventions that incorporate the needs and preferences of individual patients, regardless of diagnosis.
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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 | 0.023 | 0.080 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.028 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".