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Development of an experimental model of pre-thrombosis in rats based on Wessler's principle using a calibrated venous stasis

2003· article· en· W2001414571 on OpenAlexaff
P. Pottier, B. Planchon, F Truchaud, Georges Lefthériotis, Jean‐Marc Herbert, L. Bressolette, David Trewick, Norbert Passuti

Bibliographic record

VenueBlood Coagulation & Fibrinolysis · 2003
Typearticle
Languageen
FieldMedicine
TopicBlood Coagulation and Thrombosis Mechanisms
Canadian institutionsHotel Dieu Hospital
Fundersnot available
KeywordsVenous stasisMedicineThrombusInferior vena cavaPartial thromboplastin timeThrombosisBlood stasisVenous thrombosisCardiologyStenosisAntithrombinInternal medicineCoagulationHeparinPathology

Abstract

fetched live from OpenAlex

We have developed a model of a pre-thrombotic state in rats based on venous stasis induced by partial ligature of the inferior vena cava. The degree of stenosis was calibrated by using variations in upstream venous pressure. Different degrees of stasis were tested in order to obtain a pre-thrombotic state. Increasing doses of thromboplastin were infused. The thrombogenic potential of this model was evaluated by measuring thrombus weight and by the increase in levels of thrombin-antithrombin complexes. A pre-thrombotic state was induced by 2 h of exposure to a 40% stasis obtained by increasing by 40% the upstream venous pressure (mean thrombus weight, 0.2 +/- 0.6 mg). In these conditions of stasis, low doses of thromboplastin induced venous thrombosis (mean weight, 23 +/- 20 mg; P < 0.05). The increase in thrombus size was correlated to the rise in thrombin-antithrombin levels (r = 0.53, P < 0.001). In conclusion, we have developed the first animal model in which venous stasis can be calibrated by varying the degree of stenosis of the inferior vena cava. This model could be used to study the kinetics of biological markers of hypercoagulability, to study the pathogeny of thrombosis or to evaluate the therapeutic efficacy of new drugs in pre-clinical trials.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.179
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 head0.073
GPT teacher head0.326
Teacher spread0.253 · 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 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

Citations9
Published2003
Admission routes1
Has abstractyes

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