Prospective cohort study of <i>Helicobacter pylori</i> infection on the risk of nonalcoholic fatty liver disease
Bibliographic record
Abstract
To investigate the epidemiological connection between nonalcoholic fatty liver disease (NAFLD) and Helicobacter pylori ( H. pylori). 6,478 retired and active workers, aged 22-69 years, were included in the study. Their baseline measures of height, weight, waist measurement, body mass index, blood pressure, fasting blood sugar, plasma lipid, liver function index, glycosylated hemoglobin, abdominal ultrasonography, and findings from the line “13 C urea breath test” H. pylori test were analyzed, and follow-up with consistent baseline methods and criteria was performed annually. Over a 4-year period, the prevalence of NAFLD increased by 16.9%, with 612 (18.7%) of those who tested positive for H. pylori developing NAFLD, whereas 484 (15.1%) of those who did not test positive for H. pylori were later diagnosed with new NAFLD ( χ2 = 14.862, P < 0.05). One of the risk factors identified in the univariable Cox regression model for NAFLD was H. pylori (Hazard Ratio = 1.297; 95.0% confidence interval (CI) 1.150,1.485, P < 0.000); however, H. pylori continued to be an independent factor affecting the risk of NAFLD even after accounting for gender, age, and aspects of the metabolic syndrome (Hazard Ratio = 1.240; 95.0% CI 1.077,1.429, P = 0.003). The growth of NAFLD may be correlated with H. pylori infection.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".