Should we stop doing comprehensive geriatric assessments in patients under 75 with cancer?
Bibliographic record
Abstract
INTRODUCTION: The Older Adults with Cancer Clinic (OACC) performs comprehensive geriatric assessments (CGAs) for patients aged ≥65 with cancer. To manage limited resources and increasing wait times, raising the age cut-off from 65 to 75 was considered. Our objective was to determine whether the CGA has a similar impact on primary and secondary outcomes for patients under 75 compared to those aged 75+. MATERIALS AND METHODS: We performed a retrospective analysis of 1402 consecutive patients aged 65 or older referred from July 2015 to June 2024 in the pre-treatment setting. Data were obtained from a custom clinical database. The primary outcome of the CGA is its impact on the oncology treatment plan, and secondary outcomes include the identification of abnormalities across eight geriatric domains, CGA-based recommendations, and five care enhancements: comorbidity management, cancer treatment delivery, educational support, peri-operative management, and symptom management. Descriptive analytics were used to identify differences in the primary and secondary outcomes of the CGA for patients under 75 versus those ≥75 using chi-square tests. RESULTS: = 7.07, p = 0.008), no notable differences were observed in the percentage of patients with abnormalities across the geriatric domains. Similarly, no significant differences in CGA-based recommendations or care enhancements were found between patients under 75 and those aged ≥75 (all p > 0.05). DISCUSSION: Patients aged ≥75 are slightly more likely to undergo treatment changes following the CGA. However, the 43.5% treatment change rate among those under 75 and the lack of differences in secondary outcomes between age groups confirm the value of CGA in improving care for both age groups. Our findings suggest that among patients aged 65 or older, age should not be used to define who might benefit from a CGA.
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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 | 0.003 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".