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Record W4389398993 · doi:10.1186/s12959-023-00565-5

Comparison between canine and porcine models of chronic deep venous thrombosis

2023· article· en· W4389398993 on OpenAlexaboutno aff
Chuang Wang, Tao Tang, Sheng−Lin Ye, Nan Hu, Xiaolong Du, Xiaoqiang Li

Bibliographic record

VenueThrombosis Journal · 2023
Typearticle
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsnot available
FundersFundamental Research Funds for the Central UniversitiesGovernment of Jiangsu ProvinceChina Postdoctoral Science FoundationNatural Science Foundation of Jiangsu ProvinceNational Natural Science Foundation of China
KeywordsMedicineThrombusThrombosisDeep veinVenous thrombosisAngiologyVeinRadiologySurgeryInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: To first induce chronic deep venous thrombosis in the left iliac veins of canines and porcines and then compare these two models to validate endovascular treatment devices. METHODS: Thrombin and fibrinogen were used to produce a solid thrombus in the left iliac veins of a stenosis model. The researchers used venous angiography and histological staining to investigate the progression of thrombosis. RESULTS: A left iliac vein thrombus was successfully formed in all experimental animals, including six Labrador dogs and three Bama miniature pigs, and there was minimal surgical bleeding. All dogs survived until 90 days, and three pigs died on Days 29, 33, and 58. CONCLUSION: The researchers first established the models and then observed the progression of chronic deep venous thrombosis of the iliac vein in large animals for up to 90 days. Dogs are better suited for chronic deep venous thrombosis models due to their uncomplicated anatomy, excellent obedience, and proneness to physical activity compared with pigs.

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)0.001
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 score0.307
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
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 head0.080
GPT teacher head0.354
Teacher spread0.275 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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