Frequency of malnutrition and associated risk factors in geriatric patients in a palliative care unit
Bibliographic record
Abstract
Introduction: Our study aimed to determine the frequency of malnutrition and associated risk factors in older patients receiving palliative care. Material and methods:This prospective cross-sectional study was performed with older patients hospitalized and treated in the palliative care unit of our university between December 1, 2022 and June 1, 2023.The demographic characteristics of the patients, reasons for palliative care admission, and the referring unit were recorded.The patients' nutritional status was assessed using the full mini nutritional assessment (MNA) and the geriatric nutritional risk index (GNRI).Selected laboratory parameters were recorded at admission to the palliative care unit.Results: The 371 patients included in the study had a median age of 74 years and 58.5% were female.Malnutrition was detected more frequently in geriatric palliative care patients with cerebrovascular disease, malignancy, dementia, and congestive heart failure.Malnutrition was detected in 69.32% of the patients according to GNRI and in 64.80% according to MNA.Prealbumin had the highest specificity (92.3%) at a cut-off value of 0.175 mg/dl.The multivariate logistic regression model created with these variables showed that female sex, Ca, Cl, prealbumin, and hemoglobin were significant independent risk factors for malnutrition (p < 0.05).Female sex was associated with 2.5 times higher odds of malnutrition.Conclusions: The prevalence of malnutrition was found to be high among geriatric palliative care patients in our study.The geriatric nutritional risk index can be used to diagnose malnutrition in immobile older palliative care patients and may be applied in routine patient follow-up to help improve quality of life.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".