Evaluation of malnutrition and cognitive performance in patients with acute stroke
Bibliographic record
Abstract
Objective: To evaluate nutritional risk using Global Leadership in Malnutrition (GLIM) criteria and Mini Nutritional Assessment Test (MNA) score, cognitive performance using Montreal Cognitive Assessment (MoCA) and Mini Mental State Examination (MMSE) scores, and the association of nutritional risk with cognitive status. Methods: The study sample consists of 135 acute stroke patients hospitalized in a neurology clinic in Turkey. A questionnaire was used to determine the sociodemographic characteristics of the patients. MNA and GLIM criteria were used to evaluate nutritional status, the Modified Rankin Scale was used to determine the severity of stroke, and MMSE and MoCA tests were used to determine cognitive performance using a face-to-face interview technique. Anthropometric measurements of the patients were also taken. Results: Univariate ANOVA analysis found significant association of stroke severity and malnutrition status on cognitive performance scores separately (p<0.005). However, no significant association was observed with multivariate analysis. When various risk factors association were examined against dementia according to MMSE and MoCA, with univariate logistic regression analysis, gender, age, and education status was associatred with dementia. The risk of dementia increased 6.6 times in women and 1.1 times as age increased according to the MMSE score. The risk of dementia increased 4.2 times in women and 1.1 times as age increased according to the MoCA score. However, with multivariate analyses, it was found that only age had significant effect. Conclusion: Evaluation of cognitive function and nutritional status is essential for stroke patients. Evaluation of stroke patients with a multidisciplinary approach can contribute to the prognosis of the disease.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".