The Impact of Malnutrition and Frailty on Morbidity and Mortality in Geriatric Internal Intensive Care Patients: A Prospective Study
Bibliographic record
Abstract
Introduction: This study aims to assess the impact of malnutrition and frailty on morbidity and mortality in geriatric patients in the internal intensive care unit. Materials and Method: The study is prospective, descriptive, and cross-sectional in design, conducted at intensive care unit. Demographic data, anthropometric measurements, clinical evaluations, and laboratory parameters are recorded for each patient. Various nutritional screening tools such as the modified NUTRIC score, Subjective Global Assessment, Nutritional Risk Screening, Mini Nutritional Assessment - Short Form, and frailty assessment scales like Edmonton Frailty Scale and Clinical Frailty Scale are used. Results: The patients were divided into two groups: survivors and non-survivors. The mean Nutritional Risk Screening -2002 score was 5.20±0.70 for the survivors group and 6.15±0.57 for the non-survivors group (p<0.001). The mean Mini Nutritional Assessment - Short Form score was 8.20±0.82 for the survivors group and 6.46±1.02 for the non-survivors group (p<0.001). According to the modified NUTRIC score, 40 patients (97.6%) in the non-survivors group were at high risk of malnutrition (p<0.001). According to the Edmonton Frailty Score, in the non-survivors group, 1 patient (2.4%) was classified as light frail, 21 patients (51.2%) as mild frail, and 19 patients (46.3%) as severe frail (p<0.001). The mean Clinical Frailty Score was 5.89±0.99 for the survivors group and 8.0±0.0 for the non-survivors group (p<0.001). Conclusion: Due to the significant prevalence of malnutrition and frailty in the critical patient population being monitored in the intensive care unit, both conditions should be regularly assessed. Keywords: Intensive Care Unit; Malnutrition; Frailty; Mortality; Geriatrics.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".