Systematic review of nutrition screening and assessment in inflammatory bowel disease
Bibliographic record
Abstract
BACKGROUND: Malnutrition is prevalent in inflammatory bowel disease (IBD). Multiple nutrition screening (NST) and assessment tools (NAT) have been developed for general populations, but the evidence in patients with IBD remains unclear. AIM: To systematically review the prevalence of abnormalities on NSTs and NATs, whether NSTs are associated with NATs, and whether they predict clinical outcomes in patients with IBD. METHODS: Comprehensive searches performed in Medline, CINAHL Plus and PubMed. Included: English language studies correlating NSTs with NATs or NSTs/NATs with clinical outcomes in IBD. Excluded: Review articles/case studies; use of body mass index/laboratory values as sole NST/NAT; age < 16. RESULTS: Of 16 studies and 1618 patients were included, 72% Crohn's disease and 28% ulcerative colitis. Four NSTs (the Malnutrition Universal Screening Tool, Malnutrition Inflammation Risk Tool (MIRT), Saskatchewan Inflammatory Bowel Disease Nutrition Risk Tool (SaskIBD-NRT) and Nutrition Risk Screening 2002 (NRS-2002) were significantly associated with nutritional assessment measures of sarcopenia and the Subjective Global Assessment (SGA). Three NSTs (MIRT, NRS-2002 and Nutritional Risk Index) were associated with clinical outcomes including hospitalizations, need for surgery, disease flares, and length of stay (LOS). Sarcopenia was the most commonly evaluated NAT associated with outcomes including the need for surgery and post-operative complications. The SGA was not associated with clinical outcomes aside from LOS. CONCLUSION: There is limited evidence correlating NSTs, NATs and clinical outcomes in IBD. Although studies support the association of NSTs/NATs with relevant outcomes, the heterogeneity calls for further studies before an optimal tool can be recommended. The NRS-2002, measures of sarcopenia and developments of novel NSTs/NATs, such as the MIRT, represent key, clinically-relevant areas for future exploration.
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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 | 0.007 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".