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Inherited disorders of platelet function and challenges to diagnosis of mucocutaneous bleeding

2010· review· en· W1524846527 on OpenAlexaff
Sara J. Israels, Magdy El Ekiaby, Teresa Quiroga, Diego Mezzano

Bibliographic record

VenueHaemophilia · 2010
Typereview
Languageen
FieldMedicine
TopicPlatelet Disorders and Treatments
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsPlateletPlatelet disorderMedicineThrombastheniaBleeding diathesisClot retractionVon Willebrand diseaseMucocutaneous zoneFibrinBlood Platelet DisordersPathologyImmunologyVon Willebrand factorDiseaseThrombinPlatelet aggregation

Abstract

fetched live from OpenAlex

SUMMARY: Platelets play a pivotal role in the arrest of bleeding at sites of vascular injury. Following endothelial damage, they respond rapidly by adhesion to subendothelial matrix proteins resulting in platelet activation, spreading, aggregation, secretion and recruitment of additional platelets to form the primary haemostatic plug. This mass provides a surface for thrombin generation and fibrin mesh formation that stabilizes the clot. Careful study of patients with inherited platelet disorders and, subsequently, of informative animal models, has identified structural platelet abnormalities that have enhanced our understanding of platelet function. The investigations of rare, but severe, inherited platelet disorders have led us to the discovery of causative molecular defects. One of the most informative is the rare autosomal recessive disorder Glanzmann thrombasthenia, caused by defect or deficiency in the platelet integrin alphaIIbbeta3, resulting in absent platelet aggregation and a significant clinical bleeding diathesis. Our new challenge is to understand the mechanisms underlying more common, but less well-defined, mucocutaneous bleeding (MCB) disorders. Present diagnostic testing for platelet function disorders and von Willebrand's Disease often fails to identify the cause of bleeding in individuals with inherited MCB.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.983
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
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 head0.050
GPT teacher head0.302
Teacher spread0.252 · 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 designNot applicable
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

Citations41
Published2010
Admission routes1
Has abstractyes

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