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Record W2765649589 · doi:10.1177/1971400917736928

Assessment of clot length with multiphase CT angiography in patients with acute ischemic stroke

2017· article· en· W2765649589 on OpenAlexaff
Vanessa Polito, Roberta La Piana, Maria Cortes, Donatella Tampieri

Bibliographic record

VenueThe Neuroradiology Journal · 2017
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
Fundersnot available
KeywordsMedicineDigital subtraction angiographyRadiologyStroke (engine)AngiographyComputed tomography angiographyCollateral circulationAcute strokeInternal medicine

Abstract

fetched live from OpenAlex

Introduction Existing stroke literature demonstrates that rapid recanalization of vessels improves long-term prognosis after acute ischemic stroke. However, further optimization of the speed of the thrombectomy procedure, used to recanalize a blocked vessel, is limited by our minimal knowledge of the clot dimensions pre-procedure. Knowing the clot dimensions would allow planning of the thrombectomy procedure with the appropriate size and length of stent retriever, and determination of the correct site of the stent deployment ensuring total coverage of the clot by the stent retriever. Methods We performed a feasibility study to assess if multiphase computed tomography angiography (mCTA) can be used to estimate clot length by comparing CTA imaging data with imaging data obtained from conventional digital subtraction angiography (DSA). A retrospective chart review was performed of patients with clots in the proximal middle cerebral artery and adequate collateral circulation, who underwent both mCTA and DSA. Results Clot length was not significantly different on 3D mCTA versus mCTA MIPs, nor was it significantly different on MIP mCTA versus DSA. Pathological evidence also supported our ability to measure clot length on mCTA. Conclusions We suggest that mCTA is a reliable and valid measure of clot length in acute ischemic stroke patients.

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)0.000
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 score0.009
Threshold uncertainty score0.477

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0010.000
Research integrity0.0000.001
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 head0.010
GPT teacher head0.274
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

Citations14
Published2017
Admission routes1
Has abstractyes

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