Geriatric assessment with management for older patients with cancer receiving radiotherapy: a cluster-randomised controlled pilot study
Bibliographic record
Abstract
BACKGROUND: Geriatric assessment and management (GAM) improve outcomes in older patients with cancer treated with surgery or chemotherapy. It is unclear whether GAM may provide better function and quality of life (QoL), or be cost-effective, in a radiotherapy (RT) setting. METHODS: In this Norwegian cluster-randomised controlled pilot study, we assessed the impact of a GAM intervention involving specialist and primary health services. It was initiated in-hospital at the start of RT by assessing somatic and mental health, function, and social situation, followed by individually adapted management plans and systematic follow-up in the municipalities until 8 weeks after the end of RT, managed by municipal nurses as patients' care coordinators. Thirty-two municipal/city districts were 1:1 randomised to intervention or conventional care. Patients with cancer ≥ 65 years, referred for RT, were enrolled irrespective of cancer type, treatment intent, and frailty status, and followed the allocation of their residential district. The primary outcome was physical function measured by the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire-C30 (QLQ-C30). Secondary outcomes were overall quality of life (QoL), physical performance, use and costs of health services. Analyses followed the intention-to-treat principle. Study registration at ClinicalTrials.gov ID NCT03881137. RESULTS: We included 178 patients, 89 in each group with comparable age (mean 74.1), sex (female 38.2%), and Edmonton Frail Scale scores (mean 3.4 [scale 0-17], scores 0-3 [fit] in 57%). More intervention patients received curative RT (76.4 vs 61.8%), had higher irradiation doses (mean 54.1 vs 45.5 Gy), and longer lasting RT (mean 4.4 vs 3.6 weeks). The primary outcome was completed by 91% (intervention) vs 88% (control) of patients. No significant differences between groups on predefined outcomes were observed. GAM costs represented 3% of health service costs for the intervention group during the study period. CONCLUSIONS: In this heterogeneous cohort of older patients receiving RT, the majority was fit. We found no impact of the intervention on patient-centred outcomes or the cost of health services. Targeting a more homogeneous group of only pre-frail and frail patients is strongly recommended in future studies needed to clarify the role and organisation of GAM in RT settings.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".