A Brazilian experience of the self transglutaminase-based test for celiac disease case finding and diet monitoring
Bibliographic record
Abstract
AIM: To evaluate the effectiveness of a rapid and easy fingertip whole blood point-of-care test for celiac disease (CD) case finding and diet monitoring. METHODS: Three hundred individuals, 206 females (68.7%) and 94 males (31.3%), were submitted to a rapid and easy immunoglobulin-A-class fingertip whole blood point-of-care test in the doctor's office in order to make immediate clinical decisions: 13 healthy controls, 6 with CD suspicion, 46 treated celiacs, 84 relatives of the celiac patients, 69 patients with dyspepsia, 64 with irritable bowel syndrome (IBS), 8 with Crohn's disease and 9 with other causes of diarrhea. RESULTS: Upper gastrointestinal endoscopy with duodenal biopsies was performed in patients with CD suspicion and in individuals with positive test outcome: in 83.3% (5/6) of the patients with CD suspicion, in 100% of the patients that admitted gluten-free diet transgressions (6/6), in 3.8% of first-degree relatives (3/79) and in 2.9% of patients with dyspepsia (2/69). In all these individuals duodenal biopsies confirmed CD (Marsh's histological classification). The studied test showed good correlation with serologic antibodies, endoscopic and histological findings. CONCLUSION: The point-of-care test was as reliable as conventional serological tests in detecting CD cases and in CD diet monitoring.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".