MétaCan
Menu
Back to cohort
Record W3015774024 · doi:10.1161/strokeaha.119.028402

Improving Clinical Detection of Acute Lacunar Stroke

2020· article· en· W3015774024 on OpenAlexaff
Francesco Arba, Grant Mair, Stephen Phillips, Peter Sandercock, Joanna M. Wardlaw

Bibliographic record

VenueStroke · 2020
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsNova Scotia Health Authority
FundersMedical Research CouncilBritish Heart Foundation
KeywordsMedicineLacunar strokeStroke (engine)Acute strokeIntensive care medicineInternal medicineIschemic strokeIschemiaTissue plasminogen activator

Abstract

fetched live from OpenAlex

Background and Purpose- We aim to identify factors associated with imaging-confirmed lacunar strokes and improve their rapid clinical identification early after symptom onset using data from the IST-3 (Third International Stroke Trial). Methods- We selected patients likely to have lacunar infarcts as those presenting with: Oxfordshire Community Stroke Project lacunar syndrome; a random sample with National Institutes of Health Stroke Scale (NIHSS) score <7; and recent lacunar infarct identified on imaging by IST-3 central blinded expert panel. An independent reviewer rated brain scans of this sample and classified visible infarcts according to type, size, and location. We investigated factors associated with presence of lacunar infarct on a 24 to 48 hour follow-up scan using multivariable logistic regression and calculated sensitivity and specificity of Oxfordshire Community Stroke Project alone and in combination with NIHSS score <7. Results- We included 568 patients (330 lacunar syndrome; 147 with NIHSS score <7; 91 with lacunar infarct on baseline imaging, numbers exclude overlaps between groups), mean (±SD) age, 73.2 (±13.6) years, 316 (56%) males, and median NIHSS score 5 (IQR, 4-8). On 24 to 48 hour scan, 138 (24%) patients had lacunar infarcts, 176 (31%) other infarct subtypes, 254 (45%) no visible infarct. Higher baseline systolic blood pressure (odds ratio, 1.01 [95% CI, 1.01-1.02]) and preexisting lacunes (odds ratio, 2.29 [95% CI, 1.47-3.57) were associated with recent lacunar infarcts. Sensitivity and specificity of lacunar syndrome was modest (58% and 45%, respectively), but adding NIHSS score <7 increased specificity (99%), positive and negative predictive values (97% and 87%, respectively). Conclusions- In patients presenting within 6 hours of stroke onset, adding NIHSS score <7 to Oxfordshire Community Stroke Project lacunar syndrome classification may increase specificity for identifying lacunar stroke early after stroke onset. Our findings may help selection of patients for clinical trials of lacunar stroke and should be validated externally. Registration- URL: http://www.controlled-trials.com/; Unique identifier: ISRCTN25765518.

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)0.003
metaresearch head score (Gemma)0.013
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.027
GPT teacher head0.305
Teacher spread0.278 · 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

Citations24
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueStrokeSame topicAcute Ischemic Stroke ManagementFrench-language works237,207