How to Implement a Geriatric Assessment in Your Clinical Practice
Bibliographic record
Abstract
Cancer is a disease that mostly affects older adults. Other health conditions, changes in functional status, and use of multiple medications change the risks and benefits of cancer treatment for older adults. Several international organizations, such as the International Society of Geriatric Oncology, the European Organization for Research and Treatment of Cancer, recommend the conduct of a geriatric assessment (GA) for older adults with cancer to help select the most appropriate treatment and identify any underlying undetected medical, functional, and psychosocial issues that can interfere with treatment. The aim of this review is to describe what a GA is and how to implement it in daily clinical practice for older adults with cancer in the oncology setting. We provide an overview of commonly used tools. Key considerations in performing the GA include the resources available (staff, space, and time), patient population (who will be assessed), what GA tools to use, and clinical follow-up (who will be responsible for using the GA results for developing care plans and who will provide follow-up care). Important challenges in implementing GA in clinical practice include not having easy and timely access to geriatric expertise, patient burden of the additional hospital visits, and establishing collaboration between the GA team and oncologists regarding expectations of the population referred for GA and expected outcomes of the GA. Finally, we provide some possible interventions for problems identified during the GA.
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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 | 0.008 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
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".