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Record W1802584573 · doi:10.5853/jos.2015.17.3.221

Choosing a Hyperacute Stroke Imaging Protocol for Proper Patient Selection and Time Efficient Endovascular Treatment: Lessons from Recent Trials

2015· review· en· W1802584573 on OpenAlexaff
Jin Soo Lee, Andrew M. Demchuk

Bibliographic record

VenueJournal of Stroke · 2015
Typereview
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineMagnetic resonance imagingEndovascular treatmentRandomized controlled trialRadiologyStroke (engine)AngiographyMagnetic resonance angiographyClinical trialInclusion and exclusion criteriaSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Recently, several prospective randomized control trials regarding endovascular treatment for patients with intracranial large artery occlusions causing acute ischemic stroke have been successfully reported. Effort to minimize time delays to endovascular treatment, patient selection and the use of retrievable stent were important factors for the success of these trials. The inclusion and exclusion criteria for each of these trials did include differences in imaging protocols. In this review, we focus on the importance of baseline non-invasive angiography prior to deciding endovascular treatment. Then imaging protocols are described for each trial according to measurement of infarct volume and collateral grading.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.985
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.114
GPT teacher head0.402
Teacher spread0.289 · 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.

Study designOther design
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

Citations40
Published2015
Admission routes1
Has abstractyes

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