An Evaluation of the Usefulness of Selected Screening Methods in Assessing the Risk of Malnutrition in Patients with Inflammatory Bowel Disease
Bibliographic record
Abstract
The aim of this study was to assess the prevalence of malnutrition risk in patients with IBD using different scales to evaluate their usefulness as first-step screening tools for the diagnosis of malnutrition using the GLIM criteria in patients with inflammatory bowel disease. This study included 82 patients with IBD. The Mini Nutritional Assessment, Malnutrition Universal Screening Tool, Saskatchewan IBD-Nutrition Risk and Malnutrition Screening Tool were used to assess malnutrition risk in the study group. In order to diagnose malnutrition, the GLIM criteria were used. According to the GLIM recommendations, malnutrition was diagnosed in 60 patients with IBD (73.17%). Depending on the applied screening tools, the prevalence of moderate and/or high-risk malnutrition in patients with IBD ranged from 20.25% to 43.59%. The highest level of accuracy (ACC) was noted for the MST and MUST questionnaires (92.50% and 90%, respectively), followed by the SASKIBD-NR test (89.97%) and the MNA questionnaire (83.33%). The results of our study indicate a high prevalence of malnutrition in patients with IBD. Thus, there is a need to conduct routine assessments of malnutrition risk using validated scales. The MUST scale seems promising in the assessment of malnutrition risk in patients with IBD as a first step in the assessment of malnutrition using the GLIM criteria.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".