A Practical Guide to Geriatric Syndromes in Older Adults With Cancer: A Focus on Falls, Cognition, Polypharmacy, and Depression
Bibliographic record
Abstract
Geriatric syndromes are multifactorial conditions that are prevalent in older adults. Geriatric syndromes are believed to develop when an individual experiences accumulated impairments in multiple systems that compromise their compensatory ability. In older adults with cancer, the presence of a geriatric syndrome is common and may increase the complexity of cancer treatment. In addition, the physiologic stress of cancer and cancer treatment may precipitate or exacerbate geriatric syndromes. Common geriatric syndromes include falls, cognitive syndromes and delirium, depression, and polypharmacy. In the oncology setting, the presence of geriatric syndromes is relevant; falls and cognitive problems have been shown to be predictive of chemotherapy toxicity and overall survival. Polypharmacy and depression are more common in older adults with cancer compared with the general geriatric population. Multiple screening tools exist to identify falls, cognitive problems, polypharmacy, and depression in older adults and can be applied to the oncology setting to identify patients at risk. When recognized, several interventions exist that could be considered for this vulnerable population. We review the available evidence of four geriatric syndromes in the oncology setting, including clinical implications, validated screening tools, potential supportive care, and therapeutic interventions.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.011 |
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".