Proton Pump Inhibitor Use Before and After a Diagnosis of Inflammatory Bowel Disease
Bibliographic record
Abstract
BACKGROUND: Proton pump inhibitors (PPIs) have an impact on the gut microbiome. We investigated whether increased use of PPIs was associated with a diagnosis of inflammatory bowel disease (IBD). METHODS: The University of Manitoba IBD Epidemiology Database includes all Manitobans diagnosed with IBD between 1984 and 2018 with age-, sex-, and geography-matched control subjects and comprehensive prescription drug data from April 1995. Subjects were considered to be users if they received 2 PPI prescriptions. We assessed PPI prescriptions prediagnosis and for 3 years postdiagnosis of IBD. The absolute and relative rates were calculated and compared for PPI use pre- and post-IBD diagnosis. RESULTS: A total of 5920 subjects were diagnosed with IBD after April 1996. Rates of PPI use in control subjects increased gradually from 1.5% to 6.5% over 15 years. Persons with IBD had a higher rate of PPI use, peaking up to 17% within 1 year of IBD diagnosis with a rate ratio (RR) of 3.1 (95% confidence interval [CI], 2.9-3.3). Furthermore, persons with Crohn's disease (RR, 4.2; 95% CI, 3.7-4.6) were more likely to have been PPI users prediagnosis than persons with ulcerative colitis (RR, 2.4; 95% CI, 2.2-2.7). Important predictors of increased PPI use were older age, year of data collection, and Crohn's disease diagnosis. CONCLUSIONS: Persons with IBD have higher PPI use preceding their diagnosis. Possibly, the use of a PPI alters the gut microbiome, increasing the risk for IBD diagnosis; or persons with IBD have increased rates of dyspepsia, warranting PPI use; or some IBD symptoms are treated with PPIs whether warranted or not.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".