Bibliographic record
Abstract
BACKGROUND: The annual cost of providing care for patients in their last year of life is estimated to account for approximately 9% of the Ontario health care budget. Access to integrated, comprehensive support and pain/symptom management appears to be inadequate and inequitable. OBJECTIVE: To evaluate the cost-effectiveness of end-of-life (EoL) care interventions included in the EoL care mega-analysis. DATA SOURCES: Multiple sources were used, including systematic reviews, linked health administration databases, survey data, planning documents, expert input, and additional literature searches. REVIEW METHODS: We conducted a literature review of cost-effectiveness studies to inform the primary economic analysis. We conducted the primary economic analysis and budget impact analysis for an Ontario cohort of decedents and their families and included interventions pertaining to team-based models of care, patient care planning discussions, educational interventions for patients and caregivers, and supportive interventions for informal caregivers. The time horizon was the last year of life. Costs were in 2013 Canadian dollars. Effectiveness measures included days at home, percentage dying at home, and quality-adjusted life-days. We developed a Markov model; model inputs were obtained from a cohort of Ontario decedents assembled from Institute for Clinical Evaluative Sciences databases and published literature. RESULTS: In-home palliative team care was cost-effective; it increased the chance of dying at home by 10%, increased the average number of days at home (6 days) and quality-adjusted life-days (0.5 days), and it reduced costs by approximately $4,400 per patient. Expanding in-home palliative team care to those currently not receiving such services (approximately 45,000 per year, at an annual cost of $76-108 million) is likely to improve quality of life, reduce the use of acute care resources, and save $191-$385 million in health care costs. Results for the other interventions were uncertain. LIMITATIONS: The cost-effectiveness analysis was based in part on the notion that resources allocated to EoL care interventions were designed to maximize quality-adjusted life-years (QALY) for patients and their family, but improving QALYs may not be the intended aim of EoL interventions. CONCLUSIONS: In-home palliative team care was cost-effective, but firm conclusions about the cost-effectiveness of other interventions were not possible.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".