The role of diet in the overlap between gastroesophageal reflux disease and functional dyspepsia
Bibliographic record
Abstract
BACKGROUND/AIMS: The prevalence of functional dyspepsia partially overlaps with gastroesophageal reflux disease (GERD), and this suggests common pathogenic mechanisms. The role of diet in these conditions is still under investigation. The present study evaluated the type of diet associated with functional dyspepsia and GERD. MATERIALS AND METHODS: A representative sample of subjects was invited to the family doctors' office, and an interview-based questionnaire was administered to diagnose functional dyspepsia and GERD (using Rome III and Montreal criteria, respectively) and to evaluate eating habits and the frequency of food intake. Correlation and regressions were used for statistical analyses, and the results were presented as odds ratio and 95% confidence interval. RESULTS: In total, 184 subjects participated in a 4-month study. Functional dyspepsia was present in 7.6%, and GERD was present in 31.0%. The predictors for dyspepsia were low educational level (22.4, 3.3-150.1, p=0.001), consumption of canned food, and the use of alcoholic drinks at least weekly. The predictors for GERD were advanced age and the use of canned food (13.9, 3.6-53.9, p<0.001) or fast food (4.6, 1.7-12.1, p=0.002). CONCLUSION: This study provides new data on the overlap of GERD and functional dyspepsia and reveals that these disorders may be associated with the consumption of canned food, fast food, and alcoholic beverages.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".