Patterns of diet‐related practices and prevalence of gastro‐esophageal reflux disease
Bibliographic record
Abstract
BACKGROUND: No studies have evaluated associations between patterns of diet-related practices as determined by latent class analysis (LCA) and gastro-esophageal reflux disease (GERD). We aimed to assess this relationship in a large sample of Iranian adults. METHODS: In a cross-sectional study in 4763 adults, diet-related practices were assessed in four domains, 'meal pattern', 'eating rate', 'intra-meal fluid intake', and 'meal-to-sleep interval', using a pretested questionnaire. LCA was applied to identify classes of diet-related practices. We defined GERD as the presence of heartburn sometimes, often or always. KEY RESULTS: The prevalence of GERD in the study population was 23.5% (n = 1120). We identified two distinct classes of meal patterns: 'regular' and 'irregular', three classes of eating rates: 'moderate', 'moderate-to-slow', and 'moderate-to-fast', two major classes of fluid ingestion with meals: 'moderate' and 'much intra-meal drinking', and two classes regarding the interval between meals and sleeping: 'short' and 'long meal-to-sleep' interval. After adjustment for potential confounders, subjects with 'irregular meal pattern' had higher odds of GERD compared with subjects with 'regular meal pattern' (OR: 1.21; 1.00-1.46). However, when taking into account BMI, the association disappeared. 'Long meal-to-sleep interval' was inversely associated with GERD compared with 'short meal-to-sleep interval' (OR: 0.73; 95% CI: 0.57-0.95). 'Eating rate' and 'intra-meal fluid intake' were not significantly associated with GERD. CONCLUSIONS & INFERENCES: Our data suggest certain associations between dietary patterns and GERD. These findings warrant evaluation in prospective studies to establish the potential value of modifications in dietary behaviors for the management of GERD.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".