Self‐Screening for Malnutrition Risk in Outpatient Inflammatory Bowel Disease Patients Using the Malnutrition Universal Screening Tool (MUST)
Bibliographic record
Abstract
BACKGROUND AND AIMS: Malnutrition is common in patients with inflammatory bowel disease (IBD) and is associated with poor outcomes. Our aim is to determine if patient self-administered malnutrition screening using the malnutrition universal screening tool (MUST) is reliable by comparing patient scores with those derived from the healthcare practitioner (HCP), the gold standard. METHODS: We conducted a prospective validation study at a tertiary Canadian academic center that included 154 adult outpatients with IBD. All patients with IBD completed a self-administered nutrition screening assessment using the MUST score followed by an independent MUST assessment performed by HCPs. The main outcome measure was chance-corrected agreement (κ) of malnutrition risk categorization. RESULTS: For patient-administered MUST, the chance-corrected agreement κ (95% confidence interval [CI]) was 0.83 (0.74-0.92) when comparing low-risk and combined medium- and high-risk patients with HCP screening. Weighted κ analysis comparing all 3 risks groups yielded a κ (95% CI) of 0.85 (0.77-0.93) between patient and HCP screening. All patients were able to screen themselves. Overall, 96% of patients reported the MUST questionnaire as either very easy or easy to understand and to complete. CONCLUSION: Self-administered nutrition screening in outpatients with IBD is valid using the MUST screening tool and is easy to use. If adopted, this tool will increase utilization of malnutrition screening in hectic outpatient clinic settings and will help HCPs determine which patients require additional nutrition support.
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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 | 0.002 | 0.006 |
| 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) | 0.001 | 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".