Development of a screening tool to detect nutrition risk in patients with inflammatory bowel disease.
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Malnutrition is a known complication of Inflammatory Bowel Disease (IBD). We assessed a known screening tool, as well as developed and validated a novel screening tool, to detect nutrition risk in outpatients with IBD. METHODS AND STUDY DESIGN: The Saskatchewan IBD-Nutrition Risk (SaskIBD-NR Tool) was developed and administered alongside the Malnutrition Universal Screening Tool (MUST). Nutrition risk was confirmed by the IBD dietitian (RD) and gastroenterologist (GI). Agreement between screening tools and RD/GI assessment was computed using Cohen's kappa. RESULTS: Of the 110 patients screened, 75 (68.2%) patients had Crohn's Disease and 35 (31.8%) ulcerative colitis. Mean BMI was 26.4 kg/m2 (SD=5.8). RD/GI assessment identified 23 patients (20.9%) at nutrition risk. The SaskIBD-NR tool classified 21 (19.1%) at some nutrition risk, while MUST classified 17 (15.5%). The SaskIBD-NR tool had significant agreement with the RD/GI assessment (k 0.83, p<0.001), while MUST showed a lack of agreement (k 0.15, p=0.12). The SaskIBD-NR had better sensitivity (82.6% vs 26.1%), specificity (97.7% vs 87.4%), positive predictive value (90.5% vs 35.3%), and negative predictive value (95.5% vs 81.7%) than the MUST. CONCLUSION: The SaskIBD-NR, which assesses GI symptoms, food restriction, and weight loss, adequately detects nutrition risk in IBD patients. Broader validation is required.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".