Detection of Celiac Disease in Primary Care: A Multicenter Case-Finding Study in North America
Bibliographic record
Abstract
BACKGROUND: Celiac disease (CD) is one of the most common lifelong disorders in western countries. However, most cases remain currently undiagnosed in North America, mostly due to poor awareness of CD by primary care physicians. OBJECTIVES: The aims of this study were (a) to determine whether an active case-finding strategy in primary care could increase the frequency of CD diagnosis and (b) to determine the most common clinical presentations of the condition. METHODS: This was a multicenter, prospective study involving adult subjects during the years 2002-2004, attending one of the participating practices. All individuals with symptoms or conditions known to be associated with CD were tested for immunoglobulin A anti-transglutaminase (tTG) antibodies, and those with elevated anti-tTG were subsequently tested for IgA antiendomysial antibodies (EMA). All subjects who were positive for EMA were advised to undergo an intestinal biopsy and HLA typing. RESULTS: The study group included 737 women and 239 men, with a median age of 54.3 yr. A positive anti-tTG test was found in 30 out of 976 investigated subjects (3.07%, 95% CI 1.98-4.16). CD was diagnosed in 22 patients (18 women, 4 men). The most frequent reasons for CD screening in these 22 cases were bloating (12/22), thyroid disease (11/22), irritable bowel syndrome (7/22), unexplained chronic diarrhea (6/22), chronic fatigue (5/22), and constipation (4/22). The prevalence of CD in the serologically screened sample was 2.25% (95% CI 1.32-3.18). The diagnostic rate was low at baseline (0.27 cases per thousand visits, 95% CI 0.13-0.41) and significantly increased to 11.6 per thousand visits (95% CI 6.8-16.4, P < 0.001) following active screening implementation. CONCLUSIONS: This study demonstrates that an active case-finding strategy in the primary care setting is an effective means to improve the diagnostic rate of CD in North America.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".