The Evidence Surrounding Non-Alcoholic Fatty Liver Disease in Individuals with Cancer: A Systematic Literature Review
Bibliographic record
Abstract
Emerging evidence indicates an association between non-alcoholic fatty liver disease (NAFLD), cancer development and mortality. Cancer treatment-induced metabolic and hepatic dysfunction may be associated with increased rates of NAFLD. The review aims to investigate current evidence surrounding NAFLD in adults (≥18 years) with cancer including prevalence, effect of cancer treatments, metabolic co-morbidities, and mortality. Embase, Scopus, PubMed, and CINAHL were searched from inception to December 2021 including randomized controlled trials and observational studies. Twenty-three articles were included, comprising 142,218 participants. The overall risk of bias for observational studies was determined as low for 10 studies and neutral for 12 studies, and the RCT was determined as some concerns. The prevalence of NAFLD, based on imaging or histology, in adults with cancer ranged from 0.5 to 81.3%, with higher prevalence in breast, colorectal and gynecological cancers. Higher rates of NAFLD were also seen in patients who (i) underwent treatments-including chemotherapy and hormone therapy and/or who (ii) had higher BMI or other metabolic co-morbidities. NAFLD was associated with an increase in all-cause and cancer-related mortality. Based on review results, it is recommended that further assessment is carried out to determine whether liver screening in high-risk patients is cost effective and if interventions can be implemented to improve hepatic and health outcomes in adults with cancer.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".