Geriatric assessment in daily oncology practice for nurses and allied health professionals: An updated overview from SIOG Nursing, Allied Health and Scientists Interest Group
Bibliographic record
Abstract
Professional societies such as the International Society of Geriatric Oncology (SIOG) and the American Society of Clinical Oncology (ASCO) recommend geriatric screening and assessment for all older adults being considered for cancer treatment, to optimize cancer treatment selection and develop a supportive care plan to optimize outcomes such as reducing their risk of treatment toxicity, hospitalisations, and improvements in function and quality of life. In many centres, nurses and allied health professionals play key leadership roles in conducting geriatric screening and assessment, developing individualized care plans and monitoring their implementation. However, most nurses and allied health professionals working with older adults with cancer receive little training related to geriatric screening and assessment and require education regarding tools selection for geriatric screening and assessment. Therefore, there is a need for an up-to-date overview of geriatric screening and assessment tools for nurses and allied health professionals. In this review, we update the previous SIOG Nursing and Allied Health (NAH) Interest Group opinion paper to reflect the latest evidence related to geriatric screening and assessment. We suggest tools to assist geriatric screening and assessment (GA) as well as suggested interventions based on geriatric assessment results for the following domains: functional status, falls, nutrition, medication-related issues, distress, cognition, delirium, social support and financial status, and comorbidity. This updated overview aims to raise awareness and make accessible supports for nurses and allied health professionals on how to integrate GA and interventions into routine cancer care.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".