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 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".