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Ultrasound for the evaluation of stenosis after flow diversion

2017· article· en· W2612005140 on OpenAlexaff
Cameron M. McDougall, Khurshid Khan, Maher Saqqur, Andrew Jack, Jeremy Rempel, Carol Derksen, Yin Xi, Michael Chow

Bibliographic record

VenueJournal of NeuroInterventional Surgery · 2017
Typearticle
Languageen
FieldMedicine
TopicIntracranial Aneurysms: Treatment and Complications
Canadian institutionsUniversity of Alberta HospitalUniversity of Alberta
Fundersnot available
KeywordsMedicineUltrasoundStenosisRadiologySurgery

Abstract

fetched live from OpenAlex

BACKGROUND AND PURPOSE: Flow diversion is a relatively new strategy used to treat complex cerebral aneurysms. The optimal method for radiographic follow-up of patients treated with flow diverters has not been established. The rate and clinical implications of in-stent stenosis for these devices is unclear. We evaluate the use of transcranial Doppler ultrasound (TCD) for follow-up of in-stent stenosis. MATERIALS AND METHODS: We analyzed 28 patients treated with the Pipeline embolization device (PED) over the course of 42 months from January 2009 to June 2012. Standard conventional cerebral angiograms were performed in all patients. TCD studies were available in 23 patients. RESULTS: Angiographic and TCD results were compared and found to correlate well. CONCLUSIONS: TCD is a potentially useful adjunct for evaluating in-stent stenosis after flow diversion.

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 score0.175
Threshold uncertainty score0.397

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.092
GPT teacher head0.338
Teacher spread0.246 · 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

Citations6
Published2017
Admission routes1
Has abstractyes

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