Co-existence of non-alcoholic fatty liver disease and inflammatory bowel disease: A review article
Bibliographic record
Abstract
Emerging data have highlighted the co-existence of non-alcoholic fatty liver disease (NAFLD) and inflammatory bowel disease; both of which are increasingly prevalent disorders with significant complications and impact on future health burden. Cross-section observational studies have shown widely variable prevalence rates of co-existing disease, largely due to differences in disease definition and diagnostic tools utilised in the studies. Age, obesity, insulin resistance and other metabolic conditions are common risks factors in observational studies. However, other studies have also suggested a more dominant role of inflammatory bowel disease related factors such as disease activity, duration, steroid use and prior surgical intervention, in the development of NAFLD. This suggests a potentially more complex pathogenesis and relationship between the two diseases which may be contributed by factors including altered intestinal permeability, gut dysbiosis and chronic inflammatory response. Commonly used immunomodulation agents pose potential hepatic toxicity, however no definitive evidence exist linking them to the development of hepatic steatosis, nor are there any data on the impact of therapy and prognosis in patient with co-existent diseases. Further studies are required to assess the impact and establish appropriate screening and management strategies in order to allow early identification, intervention and improve patient outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".