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Record W2312656861 · doi:10.4244/eijv6i8a172

A novel method for the measurement of proximal fibrous cap puncture force in chronic total occlusions: the effect of increasing age

2011· article· en· W2312656861 on OpenAlexafffund
Amandeep S. Thind, Bradley H. Strauss, Aaron A. Teitelbaum, Raffi Karshafian, Michelle Ladouceur, Cari Whyne, David E. Goertz, F. Stuart Foster

Bibliographic record

VenueEuroIntervention · 2011
Typearticle
Languageen
FieldMedicine
TopicPeripheral Artery Disease Management
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsMedicineOcclusionConventional PCILesionIn vivoNuclear medicineSurgeryCardiologyMyocardial infarction

Abstract

fetched live from OpenAlex

AIMS: The composition of CTO lesions changes significantly as they age. These changes may adversely affect PCI outcome. We present a robust ex vivo technique to measure the force required to puncture the proximal cap of CTOs, and to determine whether the puncture force differs according occlusion age to examine the effects of compositional changes over time on lesion stiffness. METHODS AND RESULTS: Occlusions were created in 44 rabbit femoral arteries by thrombin injection. Between two and 15 weeks following induction, vessels were harvested and tested in a custom setup to determine the force required to puncture the proximal cap. The puncture force mean values at 2, 6, 12, and 15 weeks were 0.61N, 0.78N, 1.21N and 1.52N, respectively. The puncture force required in occlusions of ≤6 weeks was significantly lower those≥12 weeks (0.72±0.10N versus 1.45±0.13N, p<0.01). Using a cutoff point of 1N, 86% of lesions≤6 weeks of age required<1N compared to 30% of those≥12 weeks. CONCLUSIONS: We have shown an objective and reproducible testing system for measuring CTO puncture force. Puncture force is correlated with occlusion age. This technique would be useful to evaluate therapies that alter CTO composition/compliance, as well as guidewire testing.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.872
Threshold uncertainty score0.281

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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)0.0000.000

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 head0.042
GPT teacher head0.305
Teacher spread0.263 · 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

Citations22
Published2011
Admission routes2
Has abstractyes

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