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Record W2281702563 · doi:10.3390/ma9020113

An in Vitro Twist Fatigue Test of Fabric Stent-Grafts Supported by Z-Stents vs. Ringed Stents

2016· article· en· W2281702563 on OpenAlexafffund
Jing Lin, Robert Guidoin, Jia Du, Lu Wang, Graeham R. Douglas, Danjie Zhu, Mark Nutley, Perron Lygia, Ze Zhang, Yvan Douville

Bibliographic record

VenueMaterials · 2016
Typearticle
Languageen
FieldMedicine
TopicAortic aneurysm repair treatments
Canadian institutionsUniversity of CalgaryUniversité Laval
FundersFundamental Research Funds for the Central UniversitiesHigher Education Discipline Innovation ProjectNational Natural Science Foundation of ChinaUniversité Laval
KeywordsStentMaterials scienceTwistLeakage (economics)Composite materialStructural engineeringSurgeryMedicineGeometryEngineering

Abstract

fetched live from OpenAlex

Whereas buckling can cause type III endoleaks, long-term twisting of a stent-graft was investigated here as a mechanism leading to type V endoleak or endotension. Two experimental device designs supported with Z-stents having strut angles of 35° or 45° were compared to a ringed control under accelerated twisting. Damage to each device was assessed and compared after different durations of twisting, with focus on damage that may allow leakage. Stent-grafts with 35° Z-stents had the most severe distortion and damage to the graft fabric. The 45° Z-stents caused less fabric damage. However, consistent stretching was still seen around the holes for sutures, which attach the stents to the graft fabric. Larger holes may become channels for fluid percolation through the wall. The ringed stent-graft had the least damage observed. Stent apexes with sharp angles appear to be responsible for major damage to the fabrics. Device manufacturers should consider stent apex angle when designing stent-grafts, and ensure their devices are resistant to twisting.

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 score0.017
Threshold uncertainty score0.788

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.020
GPT teacher head0.281
Teacher spread0.261 · 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

Citations24
Published2016
Admission routes2
Has abstractyes

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