HLA-DQ Typing Kits in Diagnosis and Screening for Celiac Disease
Bibliographic record
Abstract
Aim: Celiac disease (CD) is strongly associated with HLA-DQ2.2, HLA-DQ2.5, and HLA-DQ8. Up to 99.7% of all CD patients are positive for either one or two of these genetic markers, demonstrating a high negative predictive value. This has led to the development of diagnostic kits that, instead of providing a full HLA-DQ typing, detect only these three HLA-DQ types. Our aim was to compare three different kits for their performance, utilization, and costs. Because 0.4-3.6% of all CD patients test positive for HLA-DQ7 and negative for the aforementioned types, information provided by the kits regarding DQ7 alpha and beta chains was evaluated as well. Materials and Methods: Fifty DNA samples previously typed with the SSCP method were analyzed using three commercial kits. Results and Discussion: All kits report hetero- or homozygosity for HLA-DQ2.5. The XeliGen kit directly detects HLA-DQ7, but is relatively expensive. The MLPA kit is the least expensive in terms of reagents and may indirectly detect HLA-DQ7. The CeliaSCAN kit is easy to use and provides indirect information about HLA-DQ7.5. Conclusion: All kits correctly identify the CD risk genes. The resources of the laboratory and the intended use should determine the preference for any of the HLA-DQ typing kits herein described.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".