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Genetic variants in ADAMTS13 as well as smoking are major determinants of plasma ADAMTS13 levels

2017· article· en· W2635609779 on OpenAlexaff
Qianyi Ma, Paula M. Jacobi, Brian T. Emmer, Colin A. Kretz, Ayse Bilge Ozel, Beth McGee, Chava Kimchi‐Sarfaty, David Ginsburg, Jun Z. Li, Karl C. Desch

Bibliographic record

VenueBlood Advances · 2017
Typearticle
Languageen
FieldImmunology and Microbiology
TopicComplement system in diseases
Canadian institutionsMcMaster UniversityThrombosis and Atherosclerosis Research Institute
FundersNational Institute of Child Health and Human DevelopmentNational Heart, Lung, and Blood InstituteNational Institutes of HealthNational Human Genome Research InstituteEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentUniversity of MichiganHoward Hughes Medical Institute
KeywordsADAMTS13Genetic variantsGeneticsBiologyGeneImmunologyGenotype

Abstract

fetched live from OpenAlex

variants in the top 2 independent linkage disequilibrium blocks (Q448E and A732V) were identified and characterized in vitro. This study uncovered specific common genetic polymorphisms that are key genetic determinants of the variation in plasma ADAMTS13 levels in healthy individuals.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.086
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.023
GPT teacher head0.295
Teacher spread0.272 · 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.

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

Citations31
Published2017
Admission routes1
Has abstractyes

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