Variation in Testing for and Incidence of Celiac Autoimmunity in Canada: A Population-Based Study
Bibliographic record
Abstract
BACKGROUND & AIMS: The incidence of biopsy-confirmed celiac disease has increased. However, few studies have explored the incidence of celiac autoimmunity based on positive serology results. METHODS: A population-based cohort study assessed testing of tissue transglutaminase antibodies (tTG-IgA) in Alberta from 2012 to 2020. After excluding prevalent cases, incident celiac autoimmunity was defined as the first positive tTG-IgA result between 2015 and 2020. Testing and incidence rates for celiac autoimmunity were calculated per 1000 and 100,000 person-years, respectively. Incidence rate ratios (IRRs) were calculated to identify differences by demographic and regional factors. Average annual percent changes (AAPCs) assessed trends over time. RESULTS: The testing rate of tTG-IgA was 20.2 per 1000 person-years and remained stable from 2012 to 2020 (AAPC, 1.2%; 95% confidence interval [CI], -0.5 to 2.9). Testing was higher in female patients (IRR, 1.66; 95% CI, 1.65-1.66), those living in metropolitan areas (IRR, 1.39; 95% CI, 1.38-1.40), and in areas of lower socioeconomic deprivation (lowest compared to highest IRR, 1.24; 95% CI, 1.23-1.25). Incidence of celiac autoimmunity was 33.8 per 100,000 person-years and increased from 2015 to 2020 (AAPC, 6.2%; 95% CI, 3.1-9.5). Among those with tTG-IgA results ≥10 times the upper limit of normal, the incidence was 12.9 per 100,000 person-years. The incidence of celiac autoimmunity was higher in metropolitan settings (IRR, 1.28; 95% CI, 1.21-1.35) and in the least socioeconomically deprived areas compared to the highest (IRR, 1.22; 95% CI, 1.14-1.32). CONCLUSIONS: Incidence of celiac autoimmunity is high and increasing, despite stable testing rates. Variation in testing patterns may lead to underreporting the incidence of celiac autoimmunity in nonmetropolitan areas and more socioeconomically deprived neighborhoods.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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".