Pitfalls and Perils of Using Administrative Databases to Evaluate the Incidence of Inflammatory Bowel Disease Overtime
Bibliographic record
Abstract
The inflammatory bowel diseases (IBD), consisting of Crohn's disease and ulcerative colitis, affect approximately 0.6% of North Americans.1 A systematic review of population-based studies on the incidence of IBD suggests that IBD has become a global disease. The systematic review also demonstrated that since 1980s, the incidence of IBD significantly decreased in only 6% of ulcerative colitis studies and in none of the Crohn's disease studies.1 Thus, the literature suggests that the incidence of IBD has predominantly been rising or stable in regions throughout the world. In this issue of Inflammatory Bowel Disease, 2 articles evaluated the incidence of IBD in 2 provinces of Canada.2,3 In the first article, Bitton et al2 used administrative health databases from Quebec (hereafter called the Quebec IBD Cohort) to demonstrate that the incidence of Crohn's disease and ulcerative colitis decreased significantly from 2001 to 2008. In contrast, Benchimol et al3 used administrative health databases from Ontario (hereafter called the Ontario IBD Cohort) to demonstrate that the incidence of IBD increased in children and adults younger than 64 years from 1999 to 2008. The divergent results may be explained by fundamental differences between Ontario and Quebec patients with IBD including differences in: genetic susceptibility4; environmental exposures such as diet and smoking5,6; socioeconomic factors7; and practice patterns of health care providers and health care delivery.8
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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 | 0.080 | 0.307 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".