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Record W4387516120 · doi:10.1177/15910199231195134

Use of the neuroform atlas for stenting of intracranial atherosclerotic disease: Clinical and angiographic outcomes

2023· article· en· W4387516120 on OpenAlexaff
Yosef Ellenbogen, Eef J. Hendriks, Spyros Karadimas, Sean Thomas O’Reilly, Zeev Itsekzon Hayosh, Rabab Alshahrani, Ronit Agid, Joanna D. Schaafsma, Timo Krings, Patrick Nicholson

Bibliographic record

VenueInterventional Neuroradiology · 2023
Typearticle
Languageen
FieldMedicine
TopicCerebrovascular and Carotid Artery Diseases
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsMedicineICADModified Rankin ScaleStentInterquartile rangeRadiologySurgeryInternal medicineIschemic strokeIschemia

Abstract

fetched live from OpenAlex

BackgroundIntracranial atherosclerotic disease (ICAD) is a potential cause of ischemic stroke. Treatment of ICAD can include intracranial stenting. There are specifically designed stents for this use-case; however, less is known about the off-label use of the Neuroform Atlas stent. In this study, we describe the outcomes of the Neuroform Atlas stent for treatment of ICAD.MethodsAdult patients with symptomatic ICAD failing best medical treatment undergoing elective intracranial stenting using the Neuroform Atlas stent between November 2018 and March 2021 were included. Patient demographics, procedure-related details and clinical and imaging outcomes were analyzed.ResultsEighteen patients met the inclusion criteria, with a mean follow-up duration of 9.6 ± 6.8 (standard deviation) months. There were two procedure-related mortalities (one massive intracranial hemorrhage and one groin site complication with sepsis). Fifteen patients were alive at the 6-month follow-up, all with satisfactory stent patency on follow-up imaging without any new ischemic events. Modified Rankin Scale at latest follow-up was 1.9 (interquartile range 5).ConclusionIn this single-center consecutive series, intracranial stenting with the Neuroform Atlas stent was a safe and effective treatment for symptomatic ICAD patients failing best medical management.

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)0.001
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 score0.001
Threshold uncertainty score0.271

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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 head0.082
GPT teacher head0.338
Teacher spread0.255 · 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

Citations5
Published2023
Admission routes1
Has abstractyes

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