MétaCan
Menu
Back to cohort
Record W4323647754 · doi:10.1039/d3bm00167a

<i>In vivo</i> evaluation of compliance mismatch on intimal hyperplasia formation in small diameter vascular grafts

2023· article· en· W4323647754 on OpenAlexafffund
Yuan Yao, Grace Pohan, Marie F.A. Cutiongco, YeJin Jeong, Joshua Kunihiro, Aung Moe Zaw, Dency David, Hanyue Shangguan, Alfred C. H. Yu, Evelyn K. F. Yim

Bibliographic record

VenueBiomaterials Science · 2023
Typearticle
Languageen
FieldMaterials Science
TopicElectrospun Nanofibers in Biomedical Applications
Canadian institutionsResearch Institute for AgingUniversity of Waterloo
FundersNational Heart, Lung, and Blood InstituteMedical Research CouncilNatural Sciences and Engineering Research Council of CanadaNational Institutes of HealthNational Research Foundation SingaporeAgency for Science, Technology and ResearchMechanobiology Institute, SingaporeUniversity of WaterlooAgence Nationale de la RechercheNational Research FoundationNational Medical Research CouncilCanada Foundation for Innovation
KeywordsIntimal hyperplasiaAnastomosisCompliance (psychology)HyperplasiaIn vivoBiocompatibilityCarotid arteriesMedicineArteryThrombosisSurgeryPathologyChemistryInternal medicineBiology

Abstract

fetched live from OpenAlex

patency. Two groups of PVA small diameter grafts with low compliance and high compliance were fabricated by dip casting method and implanted in a rabbit carotid artery end-to-side anastomosis model for 4 weeks. We demonstrated that the grafts with compliance that more closely matched with rabbit carotid artery had lower anastomotic intimal hyperplasia formation and higher graft patency compared to low compliance grafts. Overall, this study suggested that reducing the compliance mismatch between the native artery and vascular grafts is beneficial for reducing intimal hyperplasia formation.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.060
GPT teacher head0.326
Teacher spread0.266 · 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 source (direct Gemma or distilled Codex), 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

Citations30
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueBiomaterials ScienceSame topicElectrospun Nanofibers in Biomedical ApplicationsFrench-language works237,207