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Record W2293287680 · doi:10.1177/1747493016637366

Morphological classification of penetrating artery pontine infarcts and association with risk factors and prognosis: The SPS3 trial

2016· article· en· W2293287680 on OpenAlexaff
Laura Wilson, Lesly A. Pearce, Antonio Araúz, David C. Anderson, Jorge Tapia, Carlos Bazan, Oscar Benavente, Thalia S. Field

Bibliographic record

VenueInternational Journal of Stroke · 2016
Typearticle
Languageen
FieldMedicine
TopicCerebrovascular and Carotid Artery Diseases
Canadian institutionsUniversity of British Columbia
FundersNational Institute of Neurological Disorders and Stroke
KeywordsMedicineMagnetic resonance imagingFluid-attenuated inversion recoveryHyperintensityStroke (engine)Basilar arteryCardiologyInfarctionStenosisHazard ratioRadiologyNeuroimagingInternal medicineMyocardial infarctionConfidence interval

Abstract

fetched live from OpenAlex

BACKGROUND: Pontine infarcts are common and often attributed to small vessel disease ("small deep infarcts") or basilar branch atherosclerosis ("wedge shaped"). A well-described morphological differentiation using magnetic resonance images has not been reported. Furthermore, whether risk factors and outcomes differ by morphology, or whether infarct morphology should guide secondary prevention strategy, is not well characterized. METHODS: All participants in the Secondary Prevention of Small Subcortical Strokes Study with magnetic resonance imaging -proven pontine infarcts were included. Infarcts were classified as well-circumscribed small deep (small deep infarct, i.e. lacunar), paramedian, atypical paramedian, or other based on diffusion-weighted imaging, T2/fluid-attenuated inversion recovery, and T1-magnetic resonance images. Inter-rater reliability was high (90% agreement, Cohen's kappa = 0.84). Clinical and radiologic features independently associated with small deep infarct versus paramedian infarcts were identified (multivariable logistic regression). Differences in stroke risk and death were assessed using Cox proportional hazards. RESULTS: Of the 3020 patients enrolled, 644 had pontine infarcts; 619 images were available: 302(49%) small deep infarct, 245 (40%) paramedian wedge, 35 (6%) atypical paramedian, and 37 (6%) other. Among vascular risk factors, only smoking (OR 2.1, 95% CI 1.3-3.3) was independently associated with small deep infarct versus paramedian infarcts; on neuroimaging, old lacunes on T1/fluid-attenuated inversion recovery (OR 1.8, 1.3-2.6) and intracranial stenosis (any location) ≥50% (OR 0.62, 0.41-0.96). Small deep infarct versus paramedian was not predictive of either recurrent stroke or death, and there was no interaction with assigned treatment. CONCLUSIONS: Pontine infarcts can be reliably classified based on morphology using clinical magnetic resonance images. Few risk factors differed between small deep infarct and paramedian infarcts with no differences in recurrent stroke or mortality. There was no difference in response to different antiplatelet or blood pressure treatment strategies between these two groups. REGISTRATION: http://www.clinicaltrials.gov/NCT00059306.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.015
GPT teacher head0.254
Teacher spread0.239 · 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 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

Citations12
Published2016
Admission routes1
Has abstractyes

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