Body impedance analysis to estimate malnutrition in inflammatory bowel disease patients – A cross‐sectional study
Bibliographic record
Abstract
OBJECTIVE: Malnutrition is a common clinical problem in patients with inflammatory bowel diseases (IBD). However, a gold standard for the detection of malnutrition in IBD patients is lacking. METHODS: A cross-sectional study to assess malnutrition in patients with IBD and healthy controls (HCs). Clinical characteristics (Montreal classification, disease activity, previous surgery) and mutations in the NOD2 gene in patients with Crohn's disease (CD) were obtained. We performed a nutritional assessment with screening for nutritional risk and diagnosis for malnutrition (Malnutrition Universal Screening Tool [MUST]) score, NRS-2002, European Society for Clinical Nutrition and Metabolism (ESPEN), and Global Leadership Initiative on Malnutrition (GLIM) criteria and performed body impedance analysis (BIA). RESULTS: 101 IBD patients (57 CD and 44 ulcerative colitis (UC) and 50 HC were included in a single northern German tertiary center. GLIM criteria detected malnutrition significantly more often compared to the ESPEN criteria. Active disease, a long-standing disease course, and previous surgery were associated with reduced muscle mass. IBD patients had a higher fat mass index compared to HC. Mutations in the NOD2 gene had no effect on nutritional status. CONCLUSIONS: The GLIM criteria detect malnutrition at a higher rate compared to ESPEN. Specific disease factors might put IBD patients at a higher risk for the development of malnutrition, so these patients might benefit from a frequently performed screening, which might result in a favorable disease course.
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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 | 0.001 | 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) | 0.000 | 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".