Aeroallergen-related Diseases Predate the Diagnosis of Inflammatory Bowel Disease
Bibliographic record
Abstract
OBJECTIVE: This study aimed to determine whether having a diagnosis of asthma or allergic rhinitis (AR) increased the risk of being diagnosed with inflammatory bowel disease (IBD) and whether there was increased incidence of these diseases after a diagnosis of IBD. DESIGN: This is a retrospective, historical cohort-based study. We used the administrative data of Manitoba Health and the population-based University of Manitoba IBD Epidemiology Database. We used numbers of prescriptions for drugs used to treat asthma and to treat AR to identify diagnoses of asthma and AR, respectively.We calculated relative risks (RRs) to assess incidence of IBD compared with matched controls after diagnoses of asthma and AR and hazard ratios to determine the incidence of asthma and AR after IBD diagnosis. RESULTS: Compared with controls, a diagnosis of asthma or AR preceding a diagnosis of IBD was increased in cases (RR, 1.62; 95% confidence interval [CI], 1.50-1.75; and RR, 2.10; 95% CI, 1.97-2.24) with a similar outcome by subtype of IBD (Crohn's disease vs ulcerative colitis) and by sex. On sensitivity analysis, diagnoses of asthma or AR were comparable when considering at least 5, 10, 15 or 20 drug prescriptions. Persons with IBD were more likely to develop asthma or AR than controls after being diagnosed with IBD (hazard ratio for asthma, 1.31, 95% CI, 1.18-1.45; and hazard ratio for AR, 2.62, 95% CI, 2.45-2.80). CONCLUSIONS: The association between asthma, AR, and IBD suggest the possibility that whatever triggers the onset of these atopic diseases may trigger the onset of IBD as well, and aeroallergens are plausible culprits.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".