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Record W2944767973 · doi:10.1089/gtmb.2018.0329

HLA-DQ Typing Kits in Diagnosis and Screening for Celiac Disease

2019· article· en· W2944767973 on OpenAlexaff
Maxine D. Rouvroye, Sander van Zijtveld, Petra Bonnet, Eric Spierings, Hetty J. Bontkes

Bibliographic record

VenueGenetic Testing and Molecular Biomarkers · 2019
Typearticle
Languageen
FieldMedicine
TopicCeliac Disease Research and Management
Canadian institutionsInstitute of Infection and Immunity
FundersMedical Research Council
KeywordsHuman leukocyte antigenTypingMultiplexMedicineDiseaseImmunologyBioinformaticsInternal medicineAntigenBiologyGenetics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.295
Threshold uncertainty score0.490

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.288
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2019
Admission routes1
Has abstractyes

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