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Record W2084959658 · doi:10.1002/ana.20497

Triaging transient ischemic attack and minor stroke patients using acute magnetic resonance imaging

2005· article· en· W2084959658 on OpenAlexaff
Shelagh B. Coutts, Jessica Simon, Michael Eliasziw, Chul‐Ho Sohn, Michael D. Hill, Philip A. Barber, Vanessa Palumbo, Theodore A. Kennedy, Jayanta Roy, Alexis Gagnon, James N. Scott, Alastair M. Buchan, Andrew M. Demchuk

Bibliographic record

VenueAnnals of Neurology · 2005
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsFoothills Medical CentreUniversity of Calgary
Fundersnot available
KeywordsMagnetic resonance imagingStroke (engine)MedicineLesionOcclusionMinor strokeIschemiaRadiologyCardiologySurgeryStenosis

Abstract

fetched live from OpenAlex

We examined whether the presence of diffusion-weighted imaging (DWI) lesions and vessel occlusion on acute brain magnetic resonance images of minor stroke and transient ischemic attack patients predicted the occurrence of subsequent stroke and functional outcome. 120 transient ischemic attack or minor stroke (National Institutes of Health Stroke Scale < or = 3) patients were prospectively enrolled. All were examined within 12 hours and had a magnetic resonance scan within 24 hours. Overall, the 90-day risk for recurrent stroke was 11.7%. Patients with a DWI lesion were at greater risk for having a subsequent stroke than patients without and risk was greatest in the presence of vessel occlusion and a DWI lesion. The 90-day risk rates, adjusted for baseline characteristics, were 4.3% (no DWI lesion), 10.8% (DWI lesion but no vessel occlusion), and 32.6% (DWI lesion and vessel occlusion) (p = 0.02). The percentages of patients who were functionally dependent at 90 days in the three groups were 1.9%, 6.2%, and 21.0%, respectively (p = 0.04). The presence of a DWI lesion and a vessel occlusion on a magnetic resonance image among patients presenting acutely with a transient ischemic attack or minor stroke is predictive of an increased risk for future stroke and functional dependence.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.548
Threshold uncertainty score0.738

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 science0.0000.000
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 head0.038
GPT teacher head0.313
Teacher spread0.274 · 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 designNot applicable
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

Citations231
Published2005
Admission routes1
Has abstractyes

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