Prevalence of Gastroesophageal Reflux Disease in Saudi Arabia
Bibliographic record
Abstract
Background: Gastroesophageal reflux disease (GERD) is a common chronic gastrointestinal tract disease. The incidence is higher in Asian and Arab countries. In Saudi Arabia, there are few studies that have assessed the prevalence of GERD among some cities' communities. Hence, this study aims to study the prevalence of GERD among the general population of Saudi Arabia. Methods: A cross-sectional study was designed to determine the prevalence of GERD among the community of Saudi Arabia. The sample was randomly gathered through self-administered validated GERD questionnaire (GerdQ) to diagnose GERD, during the period from November to December 2016. The sociodemographic data was assessed for all participants. The data were analysed using Statistical Package for Social Sciences version 21.0 (SPSS); the t -test was used to assess the association of GERD and sociodemographic data. Results: The sample was comprised of 2,043 participants. Female and male were 51.8% and 48.2%, respectively. Mean age was 29.6 years with the standard deviation of 10.5 years. The GERD prevalence was 28.7%. It was found statistically significant among divorced/widow (34.9%, P = 0.003). In contrast, there was no association between GERD's prevalence and gender, age, residence status, education level, occupation, and blood group (P > 0.05). Conclusions: The prevalence of GERD among Saudi population is higher than that in Western countries and East Asia. It affects divorced/widow, obese and those with a sedentary lifestyle. It is advocated that national programs and educational campaigns for prevention of this disease and its complications should be established. J Clin Med Res. 2018;10(3):221-225 doi: https://doi.org/10.14740/jocmr3292w
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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) | 0.002 | 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".