Geriatric assessment and treatment decision-making in surgical oncology
Bibliographic record
Abstract
PURPOSE OF REVIEW: Present an approach for surgical decision-making in cancer that incorporates geriatric assessment by building upon the common categories of tumor, technical, and patient factors to enable dual assessment of disease and geriatric factors. RECENT FINDINGS: Conventional preoperative assessment is insufficient for older adults missing important modifiable deficits, and inaccurately estimating treatment intolerance, complications, functional impairment and disability, and death. Including geriatric-focused assessment into routine perioperative care facilitates improved communications between clinicians and patients and among interdisciplinary teams. In addition, it facilitates the detection of geriatric-specific deficits that are amenable to treatment. We propose a framework for embedding geriatric assessment into surgical oncology practice to allow more accurate risk stratification, identify and manage geriatric deficits, support decision-making, and plan proactively for both cancer-directed and non-cancer-directed therapies. This patient-centered approach can reduce adverse outcomes such as functional decline, delirium, prolonged hospitalization, discharge to long-term care, immediate postoperative complications, and death. SUMMARY: Geriatric assessment and management has substantial benefits over conventional preoperative assessment alone. This article highlights these advantages and outlines a feasible strategy to incorporate both disease-based and geriatric-specific assessment and treatment when caring for older surgical patients with cancer.
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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) | 0.001 | 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".