Risk Factors for Gastroesophageal Reflux Disease in Saudi Arabia
Bibliographic record
Abstract
Background: Gastroesophageal reflux disease (GERD) is one of the most prevalent gastrointestinal tract diseases worldwide. GERD has an effect on the patient s’ quality of life as well as the health care system that can be prevented by identifying its risk factors among the population. Hence, we applied this study to assess the GER D’s risk factors in Saudi Arabia. Methods: A cross-sectional study was designed to assess the GER D’s risk factors among the community of Saudi Arabia. The sample was collected randomly during the period from November to December 2016. Through a self-administered validated GERD questionnaire (GerdQ), GERD was diagnosed. Then, the GER D’s risk factors were assessed among all participants. The data were analyzed using Statistical Package for Social Sciences version 21.0; the Studen t’s t -test was used to assess the association of GERD and risk factors. Results: A total of 2,043 subjects participated in the study. The characteristics and behaviors of participants statistically significant with GERD were positive family history (39.3%), obese (body mass index > 30 kg/m 2 ) (39.4%), not performing weekly regular physical activities >= 30 min (31.1%) and smoking (39.3%). GERD was commonly noticed in participants on analgesics (38.4%), not taking fibers (37.4%), drinking tea (33.4%), eating greasy (31.2%) and fast food (32.7%), and these were statistically significant with GERD (P <= 0.05). Conclusion: The characteristics and behaviors associated with GERD in Saudi population are family history of GERD, obesity, sedentary lifestyle and smoking. Other common risk factors correlated with GERD are analgesics intake, no fibers intake, drinking tea, greasy and fast food intake. Gastroenterol Res. 2017;10(5):294-300 doi: https://doi.org/10.14740/gr906w
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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 | 0.000 | 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 | 0.000 | 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) | 0.003 | 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".