Correlations between nutritional indicators and cognitive function in patients with stable schizophrenia in a hospital setting
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Cognitive impairment is a core feature of schizophrenia, and it is now clear that there is a link between nutritional indicators and cognitive functioning. This study aimed to investigate correlations between three nutritional indicators (prognostic nutritional index [PNI], geriatric nutritional risk index [GNRI], and controlling nutritional status score [CONUT]) and cognitive function in hospitalized patients with stable schizophrenia. METHODS: A total of 235 patients who were hospitalized with stable schizophrenia were included. Patient demographic information was collected through self-reports or electronic medical records, and cognitive function was assessed using the Montreal Cognitive Assessment in China (MoCA-C). Information on serum albumin and total cholesterol levels, lymphocyte counts, and body mass index during the stable stage of schizophrenia was collected to calculate the PNI, GNRI, and CONUT scores, according to their respective calculation criteria. Covariate-adjusted linear regression model and ordered logistic regression model were constructed to determine the relationship between nutritional indicators and cognitive function. RESULTS: Overall, 90.2% of the patients were under the age of 60 years, and males comprised 60% of all patients. The median scores for MoCA-C, PNI, GNRI, and CONUT in hospitalized patients with stable schizophrenia were 18 (12,23), 52.85 (50.25,55.90), 110.85 (105.80,116.21), and 3 (3,3), respectively. The results of the correlation analysis showed that only PNI was associated with MoCA-C scores (r = 0.15, P = 0.021). This relationship was further confirmed by covariate-adjusted linear regression modeling (β = 0.147, 95%CI:0.049-0.351, p = 0.01) and ordered logistic regression modeling (OR = 0.054, 95%CI:0.001-0.106, p = 0.046). CONCLUSIONS: The findings revealed a significant correlation between PNI scores and MoCA-C scores in hospitalized patients with stable schizophrenia.
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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) | 0.001 | 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".