Influence of urban/rural and coastal/inland environment on the prevalence, phenotype, and clinical course of inflammatory bowel disease patients from northwest of Spain
Bibliographic record
Abstract
BACKGROUND: Data on the influence of rural/urban and coastal/inland environment on inflammatory bowel disease (IBD) are either conflicting or lacking. Our aim was to analyze whether the environment has any influence on the prevalence, phenotype, and course of IBD. MATERIALS AND METHODS: We carried out a multicenter retrospective study in 1194 IBD patients from Galicia, Spain. Urban areas were defined as those with over 25,000 inhabitants. Sex, age, family history, smoking, Montreal classification, extraintestinal manifestations, steroid dependence/refractoriness, and treatment were assessed. We used the Student's t-test/Mann-Whitney U tests to compare continuous variables and χ to compare categorical variables. Logistic regression was also used. RESULTS: Living in urban municipalities was a risk factor for Crohn's disease [relative risk (RR) 1.47; 95% confidence interval (CI) 1.25-1.73; P<0.001]; living in coastal municipalities was a protective factor for ulcerative colitis (RR 0.71; 95% CI 0.60-0.85; P<0.001). Crohn's disease patients living on the coast had more frequent ileocolonic disease and needed immunosuppressives more frequently than inland patients (RR for inland 0.65; 95% CI 0.47-0.90; P=0.008). Urban Crohn's disease patients needed immunosuppressives more frequently than rural patients (RR 1.41; 95% CI 1.04-1.92; P=0.027). Urban ulcerative colitis patients had left-sided colitis less frequently. Coastal ulcerative colitis patients more frequently had extensive colitis. CONCLUSION: Crohn's disease was found more frequently in urban and coastal areas and ulcerative colitis in inland municipalities. Place of residence may also influence phenotype and clinical course as patients living on the coast have more frequent ileocolonic Crohn's disease phenotype, extensive ulcerative colitis, and greater need for immunosuppressive therapy.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".