Economic evaluations of palliative care models: A systematic review
Bibliographic record
Abstract
BACKGROUND: Palliative care aims to improve quality of life by relieving physical, emotional, and spiritual suffering. Health system planning can be informed by evaluating cost and effectiveness of health care delivery, including palliative care. AIM: The objectives of this article were to describe and critically appraise economic evaluations of palliative care models and to identify cost-effective models in improving patient-centered outcomes. DESIGN: We conducted a systematic review and registered our protocol in PROSPERO (CRD42016053973). DATA SOURCES: A systematic search of nine medical and economic databases was conducted and extended with reference scanning and gray literature. Methodological quality was assessed using the Drummond checklist. RESULTS: We identified 12,632 articles and 5 were included. We included two modeling studies from the United States and England, and three economic evaluations from England, Australia, and Italy. Two studies compared home-based palliative care models to usual care, and one compared home-based palliative care to no care. Effectiveness outcomes included hospital readmission prevented, days at home, and palliative care symptom severity. All studies concluded that palliative care was cost-effective compared to usual care. The methodological quality was good overall, but three out of five studies were based on small sample sizes. CONCLUSION: Applicability and generalizability of evidence is uncertain due to small sample sizes, short duration, and limited modeling of costs and effects. Further economic evaluations with larger sample sizes are needed, inclusive of the diversity and complexity of palliative care populations and using patient-centered outcomes.
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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 | 0.047 | 0.187 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.015 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".