The impact of pre-treatment muscle strength and physical performance on treatment modification in older adults with cancer following comprehensive geriatric assessment
Bibliographic record
Abstract
BACKGROUND: Grip strength (GS) and the short physical performance battery (SPPB) have been shown to predict clinical outcomes in older adults with cancer. However, whether pre-treatment GS and SPPB impact treatment decisions following comprehensive geriatric assessment (CGA) is poorly understood. Our objective was to assess the impact of low GS and/or SPPB on treatment modification to initially proposed treatment plans in older adults with cancer following CGA. METHODS: This was a retrospective cohort study of older adults who had undergone CGA before receiving cancer treatment. Data were retrieved from a prospective database in an academic cancer centre and medical records. Treatment modification following CGA was defined as reduced treatment intensity or transition from active treatment to supportive care. Multivariable logistic regression assessed the impact of pre-treatment GS and SPPB on treatment modification following CGA. RESULTS: In total, 515 older adults (mean age: 80.7y) who had undergone CGA prior to cancer treatment were included. Low muscle strength and/or physical performance was observed in 66.4% of participants. Treatment was modified in 49.5% of the cohort following CGA. Low GS and/or SPPB combined was predictive of treatment modification (OR = 1.77, 95%CI = 1.07-2.90, P = 0.025) in multivariable analysis. Additional predictors of treatment modification included palliative treatment intent, comorbidities and malnutrition. CONCLUSIONS: Low GS and/or SPPB combined prior to cancer treatment predicts treatment modification in older adults with cancer and may be useful in treatment decision-making. Management of poor muscle strength and physical performance should be offered to optimize patient care and potentially improve treatment outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".