Sarcopenia and Treatment Failure in Inflammatory Bowel Disease: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND: The association between sarcopenia and treatment outcomes in inflammatory bowel disease (IBD) is currently a subject of controversy. METHODS: A systematic search was performed of PubMed, Embase, Web of Science, and the Cochrane Library for studies published until April 2023. The quality assessment of each included study was performed using the Newcastle-Ottawa Scale. RESULTS: Seventeen studies were included with 2,895 IBD patients. Sarcopenia exhibited an increased risk of treatment failure (OR=2.00, 95% CI: 1.43-2.79) and notably increased the need for surgery (OR=1.54,95%CI:1.06-2.23) as opposed to a pharmacologic treatment plan change (OR=1.19, 95% CI:0.71-2.01) among IBD patients. However, no significant association was found between sarcopenia and treatment failure in corticosteroid (OR=1.21, 95% CI: 0.55-2.64) or biologic agent (OR=1.65, 95% CI: 0.93-2.92) cohorts. Sarcopenia was also linked to elevated treatment failure risks in patients with Crohn's disease (OR=1.82, 95% CI: 1.15-2.90) and those diagnosed with ulcerative colitis (OR=2.55, 95% CI: 1.05-6.21), spanning both Asian (OR=1.88, 95% CI: 1.29-2.74) and non-Asian regions (OR=2.17, 95% CI: 1.48-3.18). CONCLUSIONS: Sarcopenia was considered a novel marker for use in clinical practice to predict treatment failure, specifically, the need for surgery in IBD patients. This distinct cohort necessitates clinical attention and tailored care strategies.
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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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.034 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".