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Intra-Operative Fenestration of Stent Grafts: A Note of Caution Based upon Preliminary In Vitro Observations

2011· article· en· W2009943105 on OpenAlexaff
Jing Lin, Robert Guidoin, Lu Wang, Bin Li, Mark Nutley, Ze Zhang, Zaiping Jing, Yvan Douville

Bibliographic record

VenueJournal of Long-Term Effects of Medical Implants · 2011
Typearticle
Languageen
FieldMedicine
TopicAortic aneurysm repair treatments
Canadian institutionsUniversity of CalgaryUniversité Laval
Fundersnot available
KeywordsFenestrationStentSurgeryMedicineBiomedical engineering

Abstract

fetched live from OpenAlex

Intra-operative fenestrations of stent grafts make more frail and elderly patients amenable to endovascular surgery but require further assessment of the viability of currently used experimental techniques. Four types of polyester fabrics currently employed in stent grafts were exposed in vitro to various protocols of fenestration: cutting, trocaring, and cantering. The resulting fenestrations were examined by gross observation, light microscopy, and scanning electron microscopy. Blunt fenestration by scissors and sharp penetration led to unpredictable apertures, impairment of the integrity of the grafts, and damage to the filaments. The fenestrations were more likely to extend in the woven fabrics, whereas the knitted fabrics were more resistant to fraying. The use of the electric cautery demonstrated the ability to create a fenestration by simultaneously perforating/cutting and edge sealing. Any safe fenestration requires a perforating method that ensures the sealing of the edge of the graft material with a well-controlled diameter. A preoperative fenestration can be tolerated, but there are risks of damage to the stent grafts when reloading the device. More elegant methods of preoperative fenestration, particularly in situ retrograde laser fenestration, are in development and deserve clinical validation.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.090
Threshold uncertainty score0.538

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0000.000

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 head0.033
GPT teacher head0.320
Teacher spread0.287 · 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 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

Citations13
Published2011
Admission routes1
Has abstractyes

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