Cost-utility of geriatric assessment in older adults with cancer: Results from the 5C trial.
Bibliographic record
Abstract
12012 Background: Geriatric assessment is a guideline-recommended approach to optimize cancer management in older adults. We conducted a cost-utility analysis alongside the recently published 5C randomized controlled trial to compare geriatric assessment and management (GAM) with usual care in older adults with cancer. Methods: The economic evaluation was conducted from a societal perspective, using a 12-month time horizon. The 5C study enrolled patients age 65+ from 8 Canadian hospitals and randomly assigned them to receive GAM or usual care. Patients were initiating systemic therapy with curative or palliative intent. Quality-adjusted life years (QALYs) were measured using the EQ-5D-5L questionnaire. Healthcare utilization was evaluated using cost diaries and chart reviews. The primary outcome, an incremental monetary benefit (INMB), was evaluated for the full sample and for preselected subgroups. Multiple imputation was used to account for missing data. The 95% confidence intervals (CI) were computed using bootstrapping. Results: The study included 350 patients, of which 173 received GAM and 177 received usual care. At 12 months, the average QALYs per patient were 0.747 and 0.753 for GAM and usual care respectively (ΔQALY = -0.006, 95%CI: -0.06 to 0.05). The total average costs per patient were $48,397 and $45,342 for GAM and usual care (ΔCost = $3,078 (95% CI: -$5,336 to $12,050). At a cost-effectiveness threshold of $50,000/QALY the INMB was negative (-$3,962; 95% CI: -$7,117 to -$794) meaning that GAM was not cost-effective compared to usual care. In a subgroup analysis the INMB was positive ($4,639, 95% CI: $61 to $8,607) for patients treated with curative intent, and the intervention was cost effective in 78% of simulations, but remained negative (i.e. not cost-effective) for patients treated with palliative intent (INMB -$13,307, 95% CI -$18,063 to -$8,264). Conclusions: This is the first cost-utility analysis of GAM in cancer. The findings showed that GAM was cost-effective for cancer patients treated with curative intent but not for patients with palliative intent. Although the primary results of the 5C trial were negative, extrapolating our costing data to positive GAM trials like GAIN, GAP70, and INTEGERATE will provide a more complete picture of the cost effectiveness of GAM in this setting. Clinical trial information: NCT03154671 .
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".