OP02 Cost-Utility Of Geriatric Assessment And Management In Older Adults With Cancer: Model-Based Economic Evaluation
Bibliographic record
Abstract
Introduction Geriatric assessment and management (GAM) is a guideline-recommended strategy for optimizing cancer management among older adults. In a recent cost-utility analysis of the Canadian 5C randomized controlled trial (RCT), GAM appeared cost effective only in selective patients. We assessed 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 We conducted a model-based economic evaluation using pooled data from four international RCTs (GAIN, GAP70, INTEGERATE, and 5C), supplemented by additional evidence from the literature. Deterministic and probabilistic analyses were performed from the healthcare payer perspective, applying a six-month time horizon. The base case and main analyses used Canadian cost data. Sensitivity analyses included per-trial scenario analyses, one-year time horizon, and the use of USA costs. We reported healthcare costs per quality-adjusted life year (QALY) and the incremental net monetary benefit (INMB) using a CAD50,000(USD36,277) per QALY threshold. Results The base case analysis using Canadian costs indicated that GAM was cost effective with an INMB of CAD1,117 (USD819) (95% credibility interval [CrI]: −CAD2,450 [−USD1,796], CAD5,035 [USD3,692]) and 70.7 percent probability of GAM being cost effective at a cost-effectiveness threshold of CAD50,000 (USD36,666) per QALY. Trial-specific results varied, with the GAP70 and INTEGERATE trials yielding positive INMB values (CAD2,635 [USD1932] and CAD2,886 [USD2,116], respectively), while 5C and GAIN resulted in negative INMB values (−CAD642 [−USD471] and −CAD268 [−USD196], respectively). Sensitivity analyses revealed that chemotherapy costs were the main driver of costs in both GAM and UC strategies. Conclusions Evidence showed that GAM is generally cost effective. However, GAM’s cost effectiveness varied across trial scenarios, driven primarily by differences in chemotherapy costs. Future research should focus on identifying key GAM components that most effectively reduce severe toxicity, hospitalizations, and chemotherapy-related costs to optimize overall cost effectiveness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".