Chemokines in Non-alcoholic Fatty Liver Disease: A Systematic Review and Network Meta-Analysis
Bibliographic record
Abstract
Background: Previous results on the relationship between nonalcoholic fatty liver disease (NAFLD) and chemokines concentrations were inconsistent. The purpose of this network meta-analysis was to evaluate the link between chemokines system and NAFLD. Methods: Relevant data, published not later than June 31, 2019, were searched in the databases of PubMed, Embase, Cochrane Library, and Web of Science. Network meta-analysis was used to rank the chemokines by surface under the cumulative ranking (SUCRA) probabilities. In addition, standardized mean differences (SMDs) with 95% confidence interval (CI) were calculated as group differences in the chemokines concentrations. Results: The search in the databases identified 46 relevant studies that investigated the relationship between 15 different chemokines and NAFLD using 4,753 patients and 4,059 controls. Results from the network meta-analysis showed that the concentrations of CCL2 and CXCL8 in the nonalcoholic fatty liver group was significantly higher than that in the control group (SMDs of 1.51 and 1.95 respectively), and the concentrations of CCL3, CCL4, CCL20, CXCL8, and CXCL10 in the nonalcoholic steatohepatitis group was significantly higher than that in the control group (SMDs of 0.90, 2.05, 2.16, 0.91, and 1.46 respectively). SUCRA probabilities showed that CXCL8 had the highest rank in nonalcoholic fatty liver for all chemokines, and CCL20 had the highest rank in non-alcoholic steatohepatitis for all chemokines. Conclusion: Elevated concentrations CCL2, CCL4, CCL20, CXCL8, and CXCL10 may be associated with the NAFLD. In this regard, more population based studies are needed to ascertain this hypothesis. Systematic Review Registration: PROSPERO: CRD42020139373
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.015 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".