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Record W2078141250 · doi:10.1097/mbc.0b013e32833464ce

Fibrinolytic parameters in children with noncatheter thrombosis: a pilot study

2010· article· en· W2078141250 on OpenAlexfundno aff
Alphan Küpesiz, Meera Chitlur, Wendy Hollon, Ozgun Tosun, Ronald Thomas, Indira Warrier, Jeanne M. Lusher, Madhvi Rajpurkar

Bibliographic record

VenueBlood Coagulation & Fibrinolysis · 2010
Typearticle
Languageen
FieldMedicine
TopicBlood Coagulation and Thrombosis Mechanisms
Canadian institutionsnot available
FundersAGE-WELL
KeywordsPathogenesisFibrinolysisThromboelastographyMedicineThrombosisFibrinPlasminogen activatorCoagulationVenous thrombosisTissue plasminogen activatorInternal medicineThrombophiliaClot formationGastroenterologyCardiologyEndocrinologyImmunology

Abstract

fetched live from OpenAlex

Although the incidence of pediatric thrombosis has increased over the last decade, noncatheter-related deep venous thrombosis (nCDVT) is rare in children. Congenital and acquired hypercoagulable states may play an important role in the pathogenesis of nCDVT. In this study, we evaluated fibrinolytic parameters by measuring individual concentrations of fibrinolytic proteins and by tissue factor initiated whole blood thromboelastography (TEG), in which a fibrin clot was lyzed by exogenously added tissue plasminogen activator (tPA). Children with nCDVT were compared with age and sex-matched controls. TAFI concentrations were significantly higher in the patient group but there was no difference in the PAI-1, tPA and lipoprotein (a) concentrations. Significantly decreased fibrinolysis was found on TEG in the patient group suggesting that hypofibrinolysis may play an important role in the pathogenesis of nCDVT in children. To our knowledge, this is the first pediatric study that has systematically evaluated the role of fibrinolysis in the pathogenesis of DVT. Given our results, the role of fibrinolysis in the pathogenesis of nCDVT in children should be further evaluated in larger studies.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
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
Bibliometrics0.0010.001
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 head0.029
GPT teacher head0.269
Teacher spread0.240 · 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

Citations12
Published2010
Admission routes1
Has abstractyes

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