OC-056 The prevalence and impact of overlapping rome iv functional gastrointestinal disorders on somatisation, quality of life, and healthcare utilisation: results from a three-country general population study
Bibliographic record
Abstract
Introduction The population prevalence of Rome IV functional gastrointestinal disorders (FGIDs) and their cumulative effect on health impairment is unknown. We used data from a large cross-sectional study of the general population to address this, and compared somatisation, quality of life (QOL), and healthcare utilisation in those fulfilling criteria for Rome IV FGIDs with non-GI and organic-GI disease controls. Method An internet-based health survey was completed by 6300 general population adults from three English-speaking countries (2100 each from US, Canada, and UK). Quota-based sampling was used to ensure equal sex, age and education distribution across the countries. The survey included questions on demographics, medication, surgical history, doctor-diagnosed organic-GI diseases, criteria for the Rome IV FGIDs, the patient health questionnaire-12 somatisation measure, and the Short Form-8 QOL questionnaire. Results Data was available for analysis from 5931 subjects (49.2% female; mean-age 47.4 years). The number of subjects having symptoms compatible with a FGID was 2083 (35%) compared to 3421 (57.7%) non-GI and 427 (7.2%) organic-GI disease controls. Per GI region, the diagnostic criteria for bowel disorders were the most frequently met of all the FGIDs (n=1665, 28.1%), followed by gastroduodenal (n=627, 10.6%), anorectal (n=440, 7.4%), esophageal (n=414, 7%), and gallbladder disorders (n=10, 0.2%). On average, the 2083 individuals who met FGID criteria qualified for 1.5 FGID diagnoses, and 742 of them (36%) qualified for FGID diagnoses in more than one GI region. The presence of FGIDs in multiple regions was associated with increasing somatisation, worse mental and physical QOL, greater use of medical therapies and a higher prevalence of abdominal surgeries (all p<0.001; see table). Notably, individuals with FGIDs in multiple regions had worse somatisation and QOL scores than organic-GI disease controls. Conclusion Roughly a third of the general adult population fulfil diagnostic criteria for a Rome IV FGID. In a third of this subset multiple GI regions are involved and this overlap is associated with increased somatisation, poorer QOL, and greater healthcare utilisation. Disclosure of Interest None Declared
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".