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Thrombus stability explains the factor V Leiden paradox: a mouse model

2019· article· en· W2988383085 on OpenAlexaff
Shana A. Shaya, Randal J. Westrick, Peter L. Gross

Bibliographic record

VenueBlood Advances · 2019
Typearticle
Languageen
FieldMedicine
TopicBlood Coagulation and Thrombosis Mechanisms
Canadian institutionsMcMaster UniversityThrombosis and Atherosclerosis Research Institute
FundersNational Heart, Lung, and Blood Institute
KeywordsThrombusMedicineFactor V LeidenPulmonary embolismEmbolizationThrombosisFemoral veinAsymptomaticEmbolismDeep veinVenous thrombosisCardiologyInternal medicineRadiologyPathology

Abstract

fetched live from OpenAlex

Humans carrying the factor V Leiden (FVL) variant have a fivefold increased risk for venous thrombosis. However, incidence of deep vein thrombosis (DVT) is proportionally greater than that of pulmonary embolism (PE) in these individuals. This is known as the FVL paradox. We hypothesized that the rate of initial DVT development is similar in FVL and noncarriers, but thrombi in FVL carriers are more stable and develop into a clinically significant DVT more often than in noncarriers. To test this, we induced thrombi in the femoral vein of wild-type (WT), heterozygous (F5L/+), and FVL homozygous (F5L/L) mice. Using intravital microscopy, thrombus size and embolization were visualized and emboli in the lungs were quantified. Compared with WT, femoral vein thrombi in F5L/+ and F5L/L mice were larger and embolized less. Total and large embolic events, the percentage of thrombus that embolized, and PE burden were significantly decreased in F5L/L mice. This suggests that in noncarriers (reflected by WT), a minor injury initially resulting in a small DVT tends to remain small and asymptomatic because of the embolization of the otherwise growing thrombus. Alternatively, the same insult in people with FVL (reflected by F5L/L) leads to thrombus growth as a result of less embolization, and thus symptomatic DVT development.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.154
Threshold uncertainty score0.557

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)0.0010.000

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 head0.286
Teacher spread0.247 · 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 designBench or experimental
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

Citations14
Published2019
Admission routes1
Has abstractyes

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