Malnutrition assessment in patients with inflammatory bowel disease
Bibliographic record
Abstract
Inflammatory bowel disease (IBD) affects over 6.8 million people worldwide and is highly associated with the development of malnutrition. Malnutrition in patients with Crohn’s disease (CD) and ulcerative colitis (UC) is often due to the following: decreased oral intake; food avoidance; side effects of medications; malabsorption; chronic enteric losses; altered anatomy from luminal surgery; and increased nutritional needs in the setting of active inflammation and a high catabolic state. Approximately 20%-80% of patients with IBD are estimated to be malnourished at some point during their disease course; this wide range is likely secondary to significant heterogeneity in the definition of malnutrition in the literature, and due to the lack of robust, validated tools to identify individuals who are malnourished. While malnutrition is traditionally thought of as under- nutrition or protein calorie malnutrition, there are other nutrition phenotypes of significance in patients with IBD including micronutrient deficiencies, sarcopenia and obesity (over-nutrition). Malnutrition is associated with poor outcomes in patients with IBD, including a high number of disease flares; impaired response to biologics; increased surgical complications; hospitalizations; and impaired quality of life, independent of disease activity. Given the significant prevalence of malnutrition, the impact it can have in patients with IBD, and its responsiveness to therapeutic interventions, it is crucial to accurately assess the nutritional status of patients at the time of diagnosis and regularly thereafter.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".