S2219 The Association of Nutritional Status with Mortality in Geriatric Patients With Cancer - A 4.5-Year Prospective Study Using Validated Screening Tools
Bibliographic record
Abstract
Introduction: Malnutrition in the elderly is under-recognized yet common, affecting 25%-60% of geriatric care facilities and 35%-65% of hospitalized adults. It is linked to higher mortality, lower quality of life, poor treatment response, and increased chemotherapy toxicity. Its impact on geriatric cancer patients is less well-known. Methods: We studied the association between nutritional status (assessed by MNA, weight loss, body mass index, and lean muscle mass) and 6- and 12-month mortality in geriatric cancer patients,adjusting for covariates. From 2019-2023, we prospectively included patients over 65 withmalignancies who completed initial and 6-month follow-up nutritional assessments. Univariate Cox regression assessed the link between nutritional status and mortality, while logistic regression evaluated 6- and 12-month mortality. Data analysis was performed using SAS 9.4 (SAS Institute Inc., Cary, NC). Results: 130 patients met the inclusion criteria, 67 were men (51.5%), with a median age of 74 years, and 86.9% were White. The median follow-up was 55.4 months (reverse Kaplan-Meier method). During follow-up, 41 patients died, and 89 were alive at their last visit. Solid malignancies were present in 76%, primarily gastrointestinal (19.2%) and hematologic (23.8%), with 43.3% at stage 3-4. Higher mortality risk was statistically associated with older age, lower independent activities of daily living total score, poor gait balance, higher Patient Health Questionnaire score, malnutrition, and history of cerebral vascular accident/transient ischemic attack CVA/TIA. Peripheral vascular disease, cerebral vascular accident (CVA)/transient ischemic attack (TIA), and dementia increased the odds of 6-month mortality, while lower independent activities of daily living scores and depression raised 12-month mortality odds. Conclusion: Malnutrition is common among the elderly and cancer patients. Long-term data on its impact on survival in cancer patients is limited. Factors associated with higher mortality risk included older age, poor gait balance, and a history of CVA/TIA. This knowledge is crucial for comprehensive care planning and risk reduction in geriatric cancer patients (see Figure 1).Figure 1.: Univariate Cox regression on overall survival since CGA evaluation: BMI: Body mass index, IADL: Independent activities of daily living, MOCA: Montreal Cognitive Assessment, PHQ: Patient Health Questionnaire, PVD: Peripheral vascular disease, CVA: cerebral vascular accident, TIA: Transient ischemic attack.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".