A317 A SYSTEMATIC REVIEW OF NUTRITION SCREENING, NUTRITION ASSESSMENT AND CLINICAL OUTCOMES IN INFLAMMATORY BOWEL DISEASE
Bibliographic record
Abstract
Malnutrition is highly prevalent in inflammatory bowel disease (IBD) but is not routinely screened or assessed. Multiple nutrition screening (NST) and assessment tools (NAT) have been developed for general populations, but the ideal tools and their predictive validity for clinical outcomes in IBD remain unclear. We hypothesize this knowledge gap may be a reason why NST and NAT are not routinely utilized in this at risk population. To provide a review of the evidence in IBD populations: 1. Correlating NST or NAT to clinical outcomes 2. Correlating NST to NAT for diagnosis of malnutrition We performed a comprehensive search strategy including Medline, CINAHL Plus and PubMed with study selection and quality assessment carried out by two independent reviewers. A third reviewer resolved disagreements. Inclusion criteria: Diagnosis of IBD; Age ≧18 years; studies correlating NST to NAT or correlating NST/NAT to clinical outcomes; RCT/case-control/cohort/cross-sectional study Exclusion criteria: Use of BMI or lab values as sole NST/NAT 1052 articles were identified from the initial search. 41 full-texts were reviewed against inclusion/exclusion criteria; 5 studies with a total of 494 patients were analyzed (CD n=447, UC n=47). Reasons for exclusion were: no predictive clinical outcomes (n=22) and no formal screening/assessment method (n=14). NST included the Nutritional Risk Screening 2002 (NRS-2002, n=1), Malnutrition Universal Screening Tool (MUST, n=1), Nutritional Risk Index (NRI, n=1), and Malnutrition Inflammation Risk Tool (MIRT, n=1). NAT included Body Impedance Analysis (BIA, n=2), Skeletal Muscle Index (SMI, n=1) and Subjective Global Assessment (SGA, n=1). Four studies assessed correlation of NST or NAT to outcomes and three studies assessed NST to NAT. Two studies demonstrated correlation between NST of MIRT with outcomes (hospitalizations [R=0.398, p=0.003], flares [R=0.299, p=0.03], surgeries [R=0.371, p=0.006], complications [R=0.333, p=0.015]) and low NRI (< 97.5) with poor response to biologics (p=0.037). Two studies found associations between NAT (low SMI, BIA [Increased skeletal muscle percentage]) and surgical complications (OR 9.24 and 0.487 respectively). Three studies demonstrated NST (MUST, NRS-2002, MIRT) correlated with BIA (FFMI), SMI and SGA. There is limited evidence correlating NST, NAT and clinical outcomes in IBD populations. Our review found statistically significant associations between NST/NAT with outcomes, and between NST with NAT, was present in all studies. Despite this, the small number of studies and differences in NST/NAT methods did not allow for further meta-analysis. Further prospective studies are necessary to evaluate the performance of these tools to determine the most effective nutrition screening/assessment algorithm for IBD patients. None
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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 | 0.012 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.008 |
| Bibliometrics | 0.015 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".