Impact of Geriatric Assessment and Management on Quality of Life, Unplanned Hospitalizations, Toxicity, and Survival for Older Adults With Cancer: The Randomized 5C Trial
Bibliographic record
Abstract
PURPOSE: American Society of Clinical Oncology recommends that older adults with cancer being considered for chemotherapy receive geriatric assessment (GA) and management (GAM), but few randomized controlled trials have examined its impact on quality of life (QOL). PATIENTS AND METHODS: The 5C study was a two-group parallel 1:1 single-blind multicenter randomized controlled trial of GAM for 6 months versus usual oncologic care. Eligible patients were age 70+ years, diagnosed with a solid tumor, lymphoma, or myeloma, referred for first-/second-line chemotherapy or immunotherapy or targeted therapy, and had an Eastern Cooperative Oncology Group performance status of 0-2. The primary outcome QOL was measured with the global health scale of the European Organisation for the Research and Treatment of Cancer QOL questionnaire and analyzed with a pattern mixture model using an intent-to-treat approach (at 6 and 12 months). Secondary outcomes included functional status, grade 3-5 treatment toxicity; health care use; satisfaction; cancer treatment plan modification; and overall survival. RESULTS: From March 2018 to March 2020, 350 participants were enrolled. Mean age was 76 years and 40.3% were female. Fifty-four percent started treatment with palliative intent. Eighty-one (23.1%) patients died. GAM did not improve QOL (global QOL of 4.4 points [95% CI, 0.9 to 8.0] favoring the control arm). There was also no difference in survival, change in treatment plan, unplanned hospitalization/emergency department visits, and treatment toxicity between groups. CONCLUSION: GAM did not improve QOL. Most intervention group participants received GA on or after treatment initiation per patient request. Considering recent completed trials, GA may have benefit if completed before treatment selection. The COVID-19 pandemic may have affected our QOL outcome and intervention delivery for some participants.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".