Effectiveness of geriatric assessment and management in older cancer patients: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Frailty and multimorbidity among older cancer patients affect treatment tolerance and efficacy. Comprehensive geriatric assessment and management is recommended to optimize cancer treatment, but its effect on various outcomes remains uncertain. OBJECTIVE: Our objective was to conduct a systematic review and meta-analysis of randomized controlled trials (RCTs) and cost-effectiveness studies comparing comprehensive geriatric assessment (with or without implementation of recommendations) to usual care in older cancer patients. METHODS: We searched MEDLINE, EMBASE, CINAHL, and Cochrane trials from inception to January 27, 2023, for RCTs and cost-effectiveness studies. Pooled estimates for outcomes were calculated using random-effects models. RESULTS: A total of 19 full-text articles representing 17 RCTs were included. Average participant age was 72-80 years, and 31%-62% were female. Comprehensive geriatric assessment type, mode of delivery, and evaluated outcomes varied across studies. Meta-analysis revealed no difference in risk of mortality (risk ratio [RR] = 1.08. 95% confidence interval [CI] = 0.91 to 1.29), hospitalization (RR = 0.92, 95% CI = 0.77 to 1.10), early treatment discontinuation (RR = 0.89, 95% CI = 0.67 to 1.19), initial dose reduction (RR = 0.99, 95% CI = 0.99 to 1.26), and subsequent dose reduction (RR = 0.87, 95% CI = 0.70 to 1.09). However, the risk of treatment toxicity was statistically significantly lower in the comprehensive geriatric assessment group (RR = 0.78, 95% CI = 0.70 to 0.86). No cost-effectiveness studies were identified. CONCLUSION: Compared with usual care, comprehensive geriatric assessment was not associated with a difference in risk of mortality, hospitalization, treatment discontinuation, and dose reduction but was associated with a lower risk of treatment toxicity indicating its potential to optimize cancer treatment in this population. Further research is needed to evaluate 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 | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.043 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".