Assessment of nutritional risk in persons with mental health disorders admitted to the acute psychiatric inpatient unit: an Italian study.
Bibliographic record
Abstract
BACKGROUND: Mental disorders can impact on several aspects of the person and therefore also on nutritional models; the literature shows that psychiatric persons are at risk of malnutrition, but the available studies are limited, particularly in Italy. AIMS: To investigate the prevalence of malnutrition by defect and metabolic syndrome in inpatients in an acute Psychiatric unit. To evaluate the characteristics and food habits of the sample. METHODS: Assessment of the risk of malnutrition (Mini Nutritional Assessment Scale) and Metabolic Syndrome (APTIII criteria) by administering a new scale to all persons aged 18+ at admission in two inpatient units of a major teaching hospital in Milan, Italy. FINDINGS: One Hundred one people were enrolled; 29.70% were malnourished or at risk of malnutrition by defect; Major Depressive Disorder, serum levels of albumin below range and low levels of appetite were significantly associated with malnutrition. 11.88% was suffering from Metabolic Syndrome, which was significantly associated with Psychotic Disorder, high levels of appetite and junk food consumption. CONCLUSIONS: The results highlighted the relevance of malnutrition in the psychiatric population and suggest the need for a systematic evaluation, in every clinical context as well as the key role of nurses.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".