The frequency of HLA-DQ7 in patients at risk of coeliac disease: A haplotype to be reckoned with for screening?
Bibliographic record
Abstract
HLA-DQ2 and -DQ8 carriers are genetically predisposed to develop coeliac disease (CD). Testing negative for HLA-DQ2/DQ8 has a high negative predictive value. Full HLA-DQ genotyping as well as tests only typing for HLA-DQ2/DQ8 are therefore used for screening at-risk populations. However, HLA-DQ7 positive and HLA-DQ2/DQ8 negative CD patients have been described in various populations. In this study we examine the relevance for HLA-DQ7 typing in a large at-risk CD population. The HLA-DQ status of all paediatric and adult patients at-risk of CD that were typed in a tertiary medical centre laboratory between 2012 and 2016 (n = 3983) was obtained. HLA-DQ7 (HLA-DQB1*03:01) positive and HLA-DQ2/DQ8 negative patients were selected. We gathered information on serology, histology and dietary status, and CD diagnosis. In total, 489/3983 patients were HLA-DQ7-positive and HLA-DQ2/DQ8 negative, and after exclusion (missing data on diet or serology/histology), 325 were included. Only one adult patient was diagnosed with CD, based on a duodenal biopsy and a clinical response to a gluten-free diet. Homozygosity was observed in 14.8 %. Based on the current cohort additional typing of HLA-DQ7 does not seem relevant for screening at-risk populations for CD in the Netherlands. It should be considered in patients with a high suspicion of CD.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".