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Record W2110260867 · doi:10.1136/jnis.2009.000505

DynaCT evaluation of in-stent restenosis following Wingspan stenting of intracranial stenosis

2009· article· en· W2110260867 on OpenAlexaff
S Moskowitz, Michael Kelly, Joseph Haynes, David Fiorella

Bibliographic record

VenueJournal of NeuroInterventional Surgery · 2009
Typearticle
Languageen
FieldMedicine
TopicCerebrovascular and Carotid Artery Diseases
Canadian institutionsRoyal University Hospital
Fundersnot available
KeywordsMedicineRestenosisStenosisStentRadiologyPercutaneousAngioplastyAngiographyCardiologyInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE AND IMPORTANCE: To describe the use of DynaCT angiographic imaging for the evaluation of Wingspan in-stent restenosis (ISR). METHODS: Two patients were treated with Wingspan stenting and percutaneous transluminal angioplasty (Patient 1 had treatment of a severe stenosis of the right middle cerebral artery and patient 2 had severe stenosis of the left intracranial internal carotid artery. Both patients developed ISR and were evaluated with high resolution DynaCT angiographic imaging. RESULTS: DynaCT demonstrated circumferential soft tissue density material distributed within the stent as the cause of the stenosis visualized with conventional angiography. CONCLUSIONS: These findings support the hypothesis that ISR is caused by neointimal proliferation, rather than vascular re-coil with stent collapse.

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)0.001
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 score0.039
Threshold uncertainty score0.461

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.000
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 head0.046
GPT teacher head0.310
Teacher spread0.264 · 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

Citations9
Published2009
Admission routes1
Has abstractyes

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