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Record W2494212646 · doi:10.1016/j.jegh.2016.07.001

Epidemiology and risk factors of uninvestigated dyspepsia, irritable bowel syndrome, and gastroesophageal reflux disease among students of Damascus University, Syria

2016· article· en· W2494212646 on OpenAlexaboutno aff
Tareq Al Saadi, Amr Idris, Tarek Turk, Mahmoud Alkhatib

Bibliographic record

VenueJournal of Epidemiology and Global Health · 2016
Typearticle
Languageen
FieldMedicine
TopicGastroesophageal reflux and treatments
Canadian institutionsnot available
Fundersnot available
KeywordsGERDMedicineIrritable bowel syndromeEpidemiologyInternal medicineRefluxGastroenterologyDiseaseBody mass indexCross-sectional studyPathology

Abstract

fetched live from OpenAlex

Uninvestigated dyspepsia (UD), irritable bowel syndrome (IBS), and gastroesophageal reflux disease (GERD) are common disorders universally.Many studies have assessed their epidemiological characteristics around the world.However, such information is not known for Syria.We aim to estimate the epidemiologic characteristics and possible risk factors for UD, IBS, and GERD among students at Damascus University, Damascus, Syria.A cross-sectional study was conducted in July-September 2015 at a campus of Damascus University.A total of 320 students were randomly asked to complete the survey.We used ROME III criteria to define UD and IBS, and Montreal definition for GERD.In total, 302 valid participants were included in the analysis.Prevalence for UD, IBS, and GERD was 25%, 17%, and 16%, respectively.Symptom overlap was present in 46 students (15%), with UD + IBS in 28 (9.3%),UD + GERD in 26 (8.6%), and IBS + GERD in 14 (4.6%) students.Eleven (3.6%) students had symptoms of UD + IBS + GERD.Each of these overlaps occurred more frequently than expected by chance.Significant risk factors included cigarettes smoking, waterpipe consumption, and body mass index <18.5 kg/m 2 for UD; female gender and three cups of coffee/d for IBS; and two cups of tea and

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.642

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.029
GPT teacher head0.349
Teacher spread0.320 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations50
Published2016
Admission routes1
Has abstractyes

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