Systematic Review With Meta-analysis: Epidemiology of Nonalcoholic Fatty Liver Disease in Patients With Inflammatory Bowel Disease
Bibliographic record
Abstract
BACKGROUND: Nonalcoholic fatty liver disease (NAFLD) is increasingly identified in patients with inflammatory bowel disease (IBD), but there are few systematic reviews and meta-analyses of the studies of NAFLD in IBD patients. METHODS: MEDLINE, Web of Science, Cochrane Library, and Scopus were searched (until August 2018) to identify observational studies that reported the prevalence and risk factors for NAFLD in IBD patients. Pooled prevalence, odds ratios (OR), mean difference (MD), and 95% confidence intervals (95% CI) were calculated. Study quality was assessed using the modified Newcastle-Ottawa scale. RESULTS: Of the 662 citations evaluated, 19 studies (including 5620 subjects) reported the prevalence of NAFLD in IBD population and were included for the analysis. The overall pooled prevalence was 27.5% (95% CI, 20.7%-34.2%). The prevalence was higher in older patients (MD = 8.22; 95% CI, 6.22-10.22), type 2 diabetes (OR = 3.85; 95% CI, 2.49-5.95), hypertension (OR = 3.18; 95% CI, 2.36-4.28), obesity (OR = 2.79; 95% CI, 1.73-4.50), insulin resistance (OR = 6.66; 95% CI, 1.28-34.77), metabolic syndrome (OR = 4.96; 95% CI, 3.05-8.05), chronic kidney disease (OR = 4.83; 95% CI, 1.79-13.04), methotrexate use (OR = 1.76; 95% CI, 1.02-3.06), surgery for IBD (OR = 1.28; 95% CI, 1.02-1.62), and longer duration of IBD (MD = 5.60; 95% CI, 2.24-8.97). CONCLUSIONS: We found that NAFLD was not uncommon in the IBD population. Older age, metabolic risk factors, methotrexate use, prior surgery, and longer duration of IBD are predictors for the presence of NAFLD in IBD. Screening of NAFLD might be recommended among IBD patients with the aforementioned factors.
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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 | 0.028 | 0.078 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.044 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".