Cost-Utility Analysis of Geriatric Assessment and Management in Older Adults With Cancer: Economic Evaluation Within 5C Trial
Bibliographic record
Abstract
PURPOSE: Geriatric assessment (GA) is a guideline-recommended approach to optimize cancer management in older adults. We conducted a cost-utility analysis alongside the 5C randomized controlled trial to compare GA and management (GAM) plus usual care (UC) against UC alone in older adults with cancer. METHODS: The economic evaluation, conducted from societal and health care payer perspectives, used a 12-month time horizon. The Canadian 5C study randomly assigned patients to receive GAM or UC. Quality-adjusted life-years (QALYs) were measured using the EuroQol five dimension-5L questionnaire and health care utilization using cost diaries and chart reviews. We evaluated the incremental net monetary benefit (INMB) for the full sample and preselected subgroups. RESULTS: A total of 350 patients were included, of whom 173 received GAM and 177 UC. At 12 months, the average QALYs per patient were 0.728 and 0.751 for GAM and UC, respectively (ΔQALY, -0.023 [95% CI, -0.076 to 0.028]). Considering a societal perspective, the total average costs (in 2021 Canadian dollars) per patient were $46,739 and $45,177 for GAM and UC, respectively (ΔCost, $1,563 [95% CI, -$6,583 to $10,403]). At a cost-effectiveness threshold of $50,000/QALY, GAM was not cost-effective compared with UC (INMB, -$2,713 [95% CI, -$11,767 to $5,801]). The INMB was positive ($2,984 [95% CI, -$7,050 to $14,179]; probability of being cost-effective, 72%) for patients treated with curative intent, but remained negative for patients treated with palliative intent (INMB, -$9,909 [95% CI, -$24,436 to $4,153]). Findings were similar considering a health care payer perspective. CONCLUSION: To our knowledge, this is the first cost-utility analysis of GAM in cancer. GAM was cost-effective for patients with cancer treated with curative but not with palliative intent. The study provides further considerations for future adoption of GAM in practice.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".