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Record W2414799060 · doi:10.1080/14779072.2016.1196134

Imaging in acute stroke

2016· review· en· W2414799060 on OpenAlexaffabout
Prasanna Venkatesan Eswaradass, Ramana Appireddy, James Evans, Carol Huilian Tham, Sadanand Dey, Mohamed Najm, Bijoy K. Menon

Bibliographic record

VenueExpert Review of Cardiovascular Therapy · 2016
Typereview
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsHotchkiss Brain InstituteOntario Brain InstituteUniversity of Calgary
Fundersnot available
KeywordsMedicineStroke (engine)Acute strokeIschaemic strokePerfusion scanningNeuroimagingAngiographyIntensive care medicineRadiologyInternal medicinePerfusionIschemiaTissue plasminogen activator

Abstract

fetched live from OpenAlex

INTRODUCTION: Stroke is the third leading cause of death and disability in Canada. In the hyperacute stroke setting, the treating physician must make a time critical decision on the treatment of each patient. Recent advances in imaging help the treating physician identify the subgroup of patients eligible for acute treatment of ischaemic stroke. AREAS COVERED: In this review we will discuss Non-Contrast Computed Tomography (NCCT), CT-Angiography (CTA), and CT-Perfusion (CTP) in assessment of patients with acute ischaemic stroke and intracerebral haemorrhage. Intravenous tPA was the only proven therapy for acute ischaemic stroke presenting within 4.5 hours, until the five recent trials proved the efficacy of EVT for acute ischaemic stroke with proximal arterial occlusion. Imaging played a major role in patient selection in all five trials. Expert commentary: The challenge of rapid clinical assessment, review of imaging and timely treatment will continue to be made easier as the development and understanding of imaging progresses.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0070.003

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 head0.024
GPT teacher head0.346
Teacher spread0.322 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations10
Published2016
Admission routes2
Has abstractyes

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