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Record W2932938207 · doi:10.3389/fimmu.2019.00662

The Prevalence of Anti-Hexokinase-1 and Anti-Kelch-Like 12 Peptide Antibodies in Patients With Primary Biliary Cholangitis Is Similar in Europe and North America: A Large International, Multi-Center Study

2019· article· en· W2932938207 on OpenAlexaff
Gary L. Norman, A. Reig, Odette Viñas, Michael Mähler, Ewa Wunsch, Piotr Milkiewicz, Mark G. Swain, Andrew L. Mason, Laura M. Stinton, Maria Belen Aparicio, Maria Jose Aldegunde, Marvin J. Fritzler, Albert Parés

Bibliographic record

VenueFrontiers in Immunology · 2019
Typearticle
Languageen
FieldMedicine
TopicLiver Diseases and Immunity
Canadian institutionsUniversity of AlbertaUniversity of Calgary
Fundersnot available
KeywordsPrimary biliary cirrhosisAutoantibodyAntibodyMedicineImmunofluorescenceImmunologyAntigenInternal medicine

Abstract

fetched live from OpenAlex

Primary biliary cholangitis (PBC), formerly known as primary biliary cirrhosis, is present worldwide. Autoantibodies, in particular anti-mitochondrial antibodies (AMA) detected by indirect immunofluorescence assays or newer solid phase immunoassays can detect most, but not all individuals with PBC. Detection of antibodies to the anti-nuclear antigens sp100 and gp210 can identify additional PBC patients, but some seronegative patients remain, often resulting in delayed diagnosis and treatment. Antibodies to kelch-like 12 (KLHL12) and hexokinase 1 (HK-1) were recently identified as new biomarkers for PBC and notably identify patients who are negative for conventional autoantibodies. To become globally adopted, it is important to validate these new biomarkers in different geographic areas. In the present study we evaluated the prevalence of anti-KLHL12 (measured by a KLHL12-derived peptide referred to as KL-p) and anti-HK-1 antibodies by ELISA at five sites within Europe and North America and demonstrated the presence of these antibodies in patients with PBC in all geographies.

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)0.000
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 score0.010
Threshold uncertainty score0.492

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.007
GPT teacher head0.221
Teacher spread0.214 · 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

Citations25
Published2019
Admission routes1
Has abstractyes

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