Cost-Utility of Geriatric Assessment and Management in Older Adults With Cancer: Model-Based Economic Evaluation
Bibliographic record
Abstract
PURPOSE: Geriatric assessment and management (GAM) is a guideline-recommended strategy for optimizing cancer management among older adults. A recent cost-utility analysis of the Canadian 5C randomized controlled trial (RCT) found GAM to be cost effective for selected groups. This study aimed to assess the cost-utility of GAM plus usual care (UC) versus UC alone in older adults with cancer using a decision model and best available evidence from four international RCTs-GAIN, GAP70, INTEGERATE, and 5C. METHODS: For the model, we used pooled data from four RCTs and peer-reviewed literature. Deterministic and probabilistic analyses were performed from the Canadian health care payer perspective, applying a 6-month time horizon. Sensitivity analyses included per-trial scenario analyses, 1-year time horizon, and US health care payer perspective. We reported costs per quality-adjusted life year (QALY) and incremental net monetary benefit (INMB). RESULTS: The base-case analysis indicated that GAM had an INMB of $599 in Canadian dollars (CAD; 95% credibility interval, -$3,428 to $4,742) with 60.9% probability of being cost effective at a threshold of $50,000 (CAD) per QALY. Trial-specific results varied, with the GAP70 and INTEGERATE trials yielding positive INMB ($2,231 [CAD] and $2,104 [CAD], respectively), suggesting cost-effectiveness, whereas 5C and GAIN resulted in negative INMB (-$489 [CAD] and -$234 [CAD], respectively). Chemotherapy and hospitalization costs were the main driver of costs in both strategies. CONCLUSION: GAM is overall cost effective, with results varying across trial scenarios due to differences in chemotherapy dose intensity, hospitalization rates, and associated costs. Future research should prioritize identifying optimal core GAM components, delivery mode, and patient selection criteria to enhance its effectiveness and cost-effectiveness.
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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 | 0.016 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".