High Prevalence of Malnutrition and Micronutrient Deficiencies in Patients With Inflammatory Bowel Disease Early in Disease Course
Bibliographic record
Abstract
BACKGROUND: Patients with inflammatory bowel disease (IBD) are at an increased risk of malnutrition. The goal of this study was to define the prevalence of malnutrition and micronutrient deficiencies in recently diagnosed IBD patients and to compare the performance of existing malnutrition screening tools in identifying IBD patients at increased risk for malnutrition. METHODS: This was a retrospective cohort study of adult patients with recently diagnosed IBD (≤18 months disease duration). A diagnosis of malnutrition was made utilizing the European Society for Clinical Nutrition and Metabolism malnutrition criteria. Serum micronutrient levels were included. The sensitivity of 5 malnutrition screening tools in identifying patients at moderate-high risk of malnutrition was determined based on the European Society for Clinical Nutrition and Metabolism malnutrition definition. Descriptive statistics summarized the data and univariate analyses tested associations. RESULTS: A total of 182 patients were included for analysis; 65 (36%) met criteria for malnutrition. A total of 135 (74%) patients had ≥1 micronutrient level checked and 105 (78%) had ≥1 deficiency. Patients with prior surgery (odds ratio [OR], 4.5; P = .004), active Crohn's disease (OR, 2.8; P = .03), and diarrhea (OR, 2.1; P = .02) were more likely to be malnourished. The Malnutrition Universal Screening Tool and Saskatchewan IBD Nutrition Risk Tool had the highest sensitivity (100%) in predicting those at moderate-high risk of malnutrition at the time of screening. CONCLUSIONS: Patients with recently diagnosed IBD have a high prevalence of malnutrition and micronutrient deficiencies. Both the Malnutrition Universal Screening Tool and Saskatchewan IBD Nutrition Risk Tool can be used to identify those at increased risk of malnutrition. Future studies and screening tool development are necessary to identify those at risk of developing malnutrition to facilitate timely referral for nutritional evaluation and prevent disease related complications.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".