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Record W7098389694

daily life: the Systematic Investigation of Gastrointestinal Diseases in China (SILC) epidemiological study

2013· article· en· W7098389694 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPhytochemistry and Biological Activities
Canadian institutionsnot available
Fundersnot available
KeywordsHeartburnGERDEpidemiologyRegurgitation (circulation)RefluxDiseaseQuality of life (healthcare)
DOInot available

Abstract

fetched live from OpenAlex

Background: Gastroesophageal reflux disease imposes a significant burden of illness in Western populations. However, data on the impact of reflux symptoms on daily life in Asian populations are scarce. The current study aimed to evaluate the impact of GERD (defined on the basis of symptoms) on health-related quality-of-life (HRQoL) in individuals from five regions in China, as part of the Systematic Investigation of Gastrointestinal Diseases in China (SILC) study. Methods: In total, 18 000 residents were randomly selected from across five regions of China and asked to complete a general information questionnaire and a Chinese version of the Reflux Disease Questionnaire (RDQ). A randomly selected subsample of one-fifth of subjects (20 % from each region) completed Chinese versions of the 36-item self-administered (SF-36) questionnaire and Epworth Sleepiness Scale (ESS) questionnaire. Reflux symptoms were defined as the presence of heartburn and/or regurgitation. Symptom-defined GERD was diagnosed as mild heartburn and/or regurgitation ≥2 days per week, or moderate/severe heartburn and/or regurgitation ≥1 daya week, based on the Montreal Definition of GERD for population-based studies. Results: The response rate was 89.4 % for the total sample (16 091/18 000), and for the 20 % subsample (3219/ 3600). Meaningful impairment was observed in all 8 SF-36 dimensions in participants with symptom-defined GERD,

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)0.000
metaresearch head score (Gemma)0.001
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 score0.071
Threshold uncertainty score0.457

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.224
Teacher spread0.195 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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