Dietary habits and Helicobacter pylori infection: a cross sectional study at a Lebanese hospital
Bibliographic record
Abstract
BACKGROUND: To examine the association between dietary habits and Helicobacter pylori (H. pylori) infection among patients at a tertiary healthcare center in Lebanon. METHODS: This cross-sectional study was conducted on 294 patients in 2016, at a hospital in Northern Lebanon. Participants were interviewed using a structured questionnaire to collect information on socio-demographic and lifestyle characteristics; dietary habits were ascertained via a short food frequency questionnaire (FFQ). H. pylori status (positive vs. negative) was determined after upper GI endoscopy where gastric biopsy specimens from the antrum, body, and fundus region were collected and then sent for pathology analysis. Multivariable logistic regression was conducted to identify the association between socio-demographic, lifestyle, dietary and other health-related variables with H pylori infection. RESULTS: The prevalence of H. pylori infection was found to be 52.4% in this sample. Results of the multivariable analysis showed that H. pylori infection risk was higher among participants with a university education or above (OR = 2.74; CI = 1.17-6.44), those with a history of peptic ulcers (OR = 3.80; CI = 1.80-8.01), gastric adenocarcinoma (OR = 3.99; CI = 1.35-11.83) and vitamin D level below normal (OR = 29.14; CI = 11.77-72.13). In contrast, hyperglycemia was protective against H. pylori (OR = 0.18; CI = 0.03-0.89). No relationship between dietary habits and H. pylori infection was found in the adjusted analysis. CONCLUSIONS: Socio-demographic and clinical variables are found to be associated with H. pylori, but not with dietary factors. Further studies are needed to investigate the effect of diet on H. pylori risk.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".