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Record W1968155456 · doi:10.1055/s-0033-1334466

Genetic Loci Associated with Platelet Traits and Platelet Disorders

2013· review· en· W1968155456 on OpenAlexafffund
Natalia Bunimov, Nola Fuller, Catherine P.M. Hayward

Bibliographic record

VenueSeminars in Thrombosis and Hemostasis · 2013
Typereview
Languageen
FieldMedicine
TopicPlatelet Disorders and Treatments
Canadian institutionsHamilton Regional Laboratory Medicine ProgramMcMaster University
FundersCanadian Institutes of Health ResearchCanadian Hemophilia SocietyHeart and Stroke Foundation of Canada
KeywordsPlatelet disorderGenome-wide association studyPlateletBiologyGeneticsBlood Platelet DisordersPhenotypeGeneGenetic associationImmunologyGenotypeSingle-nucleotide polymorphismPlatelet aggregation

Abstract

fetched live from OpenAlex

Genetic investigations have led to important advances in our knowledge of genes, proteins, and microRNA that influence circulating platelet counts, platelet size, and function. The application of genome-wide association studies (GWAS) to platelet traits has identified multiple loci with a significant association to platelet number, size, and function in aggregation and granule secretion assays. Moreover, the genes altered by disease-causing mutations have now been identified for several platelet disorders, including X-linked recessive, autosomal dominant, and autosomal recessive platelet disorders. Some mutations that cause inherited platelet disorders involve genes that GWAS have associated to platelet traits. Although disease-causing mutations in many rare and syndromic causes of platelet disorders have now been characterized, the genetic mutations that cause common inherited platelet disorders, and impair platelet aggregation and granule secretion, are largely unknown. This review summarizes current knowledge on the genetic loci that influence platelet traits, including the genes with well-characterized mutations in certain inherited platelet disorders.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.989
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.039
GPT teacher head0.313
Teacher spread0.274 · 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 designOther design
Domainnot available
GenreReview

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

Citations33
Published2013
Admission routes2
Has abstractyes

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