Association between Helicobacter pylori infection and nonalcoholic fatty liver disease: a systemic review and meta-analysis
Bibliographic record
Abstract
Although clinical studies have shown possible links of Helicobacter pylori infection with the development of nonalcoholic fatty liver disease (NAFLD), the results remain controversial. The aim of this meta-analysis is to investigate the association between H. pylori infection and NAFLD. A comprehensive search of relevant studies was performed up to November 2018. Data on H. pylori infection in NAFLD patients and controls were extracted. Odds ratio (OR) and 95% confidence interval (CI) were calculated using a random-effects model. Twelve studies involving 27 400 NAFLD patients and 60 347 controls were included. The pooled overall OR of H. pylori infection in NAFLD patients compared with controls was 1.36 (95% CI: 1.22-1.53, I=89.6%, P=0.000). Meta-regression and subgroup analysis showed that the sample size and the case-control ratio may have accounted for some of the heterogeneity. When stratified by publication year, the diagnostic method used for H. pylori, and Newcastle-Ottawa Scale scores, the OR remained significant. However, possible publication bias was observed. Of the 12 studies, six had carried out multivariable analysis after adjusting for potential confounders. The pooled results from these studies still indicated a higher risk of NAFLD in patients infected with H. pylori (OR=1.17, 95% CI: 1.01-1.36, I=72.4%, P=0.003). There is a 36% increased risk of NAFLD in patients with H. pylori infection. Further studies are warranted to investigate whether eradication of H. pylori is useful in the prevention and treatment of NAFLD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.030 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| 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".