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Record W2990226735 · doi:10.1161/strokeaha.119.027272

Carotid Plaque With High-Risk Features in Embolic Stroke of Undetermined Source

2019· review· en· W2990226735 on OpenAlexafffund
Joseph Kamtchum‐Tatuene, Alan H. Wilman, Maher Saqqur, Ashfaq Shuaib, Glen C. Jickling

Bibliographic record

VenueStroke · 2019
Typereview
Languageen
FieldMedicine
TopicCerebrovascular and Carotid Artery Diseases
Canadian institutionsWomen and Children’s Health Research InstituteUniversity of Alberta
FundersNational Institute of Neurological Disorders and StrokeCanadian Institutes of Health Research
KeywordsMedicineStenosisStroke (engine)Embolic strokeOdds ratioStroke riskCardiologyInternal medicineRadiologyCarotid arteriesEmbolismIschemic strokeIschemia

Abstract

fetched live from OpenAlex

Background and Purpose- An ipsilateral mild carotid stenosis, defined as plaque with <50% luminal narrowing, is identified in nearly 40% of patients with embolic stroke of undetermined source and could represent an unrecognized source of atheroembolism. We aimed to summarize data about the frequency of mild carotid stenosis with high-risk features in embolic stroke of undetermined source. Methods- We searched Pubmed and Ovid-Embase for studies reporting carotid plaque imaging features in embolic stroke of undetermined source. The prevalence of ipsilateral and contralateral mild carotid stenosis with high-risk features was pooled using random-effect meta-analysis. Results- Eight studies enrolling 323 participants were included. The prevalence of mild carotid stenosis with high-risk features in the ipsilateral carotid was 32.5% (95% CI, 25.3-40.2) compared with 4.6% (95% CI, 0.1-13.1) in the contralateral carotid. The odds ratio of finding a plaque with high-risk features in the ipsilateral versus the contralateral carotid was 5.5 (95% CI, 2.5-12.0). Conclusions- Plaques with high-risk features are 5 times more prevalent in the ipsilateral compared with the contralateral carotid in embolic stroke of undetermined source, suggesting a relationship to stroke risk.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.896
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
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 head0.014
GPT teacher head0.271
Teacher spread0.257 · 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 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

Citations102
Published2019
Admission routes2
Has abstractyes

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