Desnutrición y riesgo de mortalidad en pacientes adultos mayores con cáncer en el Perú
Bibliographic record
Abstract
Objective: Malnutrition is common among cancer patients and even more prevalent in olderadults. Although its association with mortality risk is well established, further studies in LatinAmerica are needed to delve deeper into this relationship. This study aimed to evaluate theassociation between malnutrition and mortality risk among Peruvian older adult patients (≥ 60 years)with cancer. Materials and methods: Comprehensive geriatric assessment (CGA) records of olderadult patients with cancer evaluated at the Geriatrics Department of Hospital Almenara in Lima,Peru, from 2018 to January 2024, were reviewed. Nutritional status was assessed using the MiniNutritional Assessment Short Form (MNA-SF), which classifies patients into three groups: normal,at risk of malnutrition, or malnourished. This retrospective observational study comprised bothcases (patients at risk of malnutrition or malnourished) and controls. Results: The study included 171 patients (mean age of 77.8 ± 7.5 years), 60 % of whom were men. According to the MNA-SF,66 patients (38.5 %) were classified as having normal nutritional status, 77 (45.0 %) as at risk of malnutrition, and 28 (16.4 %) as malnourished. The frequency of malnutrition was higher among males (60.7 %) than females (39.3 %) (p < 0.05). The mean follow-up period was 41.1 ± 20.9 months, ranging from 0.9 to 68 months. The most common cancertypes were colorectal (23.4 %), prostate (13.5 %), stomach (11.7 %), skin (11.7 %), breast (9.4 %), non-Hodgkin lymphoma (5.4 %), head and neck (4.7 %), lung (3.5 %), endometrial (3.5 %), and pancreatic (2.9 %). In the multivariate Cox regression analysis,patients who were at risk of malnutrition or malnourished had a higher mortality risk compared to those with normal nutritional status (hazard ratio [HR], 2.9; 95 % confidence interval [CI], 1.37–7.26; p < 0.01). Conclusions: Peruvian older adult patientswith cancer at risk of malnutrition or malnourished have a higher mortality risk compared with their counterparts with normal nutritional status.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".