Comprehensive Geriatric Assessment: A Case Report on Personalizing Cancer Care of an Older Adult Patient With Head and Neck Cancer
Bibliographic record
Abstract
BACKGROUND: Understanding multidimensional screening and assessment is key to optimizing cancer care in older adults. OBJECTIVES: This article aims to present comprehensive geriatric assessment (CGA) as an approach to personalizing care for older adults with cancer. METHODS: A case study of an 89-year-old man with head and neck cancer is presented as a framework to describe the process of CGA and an overview of geriatric oncology screening and assessment. FINDINGS: CGA enables personalized care by informing decision making about cancer treatment and guiding implementation of enhanced supportive interventions. Screening tools can help identify older adult patients who would benefit from CGA. Oncology nurses can integrate geriatric assessment tools into practice to identify and address age-related concerns, facilitate communication, and contribute to personalization of 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".