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Record W4406230220 · doi:10.1136/bmjopen-2024-087704

Importance of infarct topography in determination of stroke mechanism and recurrence risk: a post-hoc analysis of the dabigatran acute treatment of stroke trial

2025· article· en· W4406230220 on OpenAlexafffund
Erol Cimen, Kelvin Kuan Huei Ng, Brian Buck, Thalia S. Field, Shelagh B. Coutts, Laura C. Gioia, Michael D. Hill, Jodi Miller, Oscar Benavente, Mukul Sharma, Kenneth Butcher

Bibliographic record

VenueBMJ Open · 2025
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsUniversity of British ColumbiaUniversity of CalgaryUniversité de MontréalPopulation Health Research InstituteUniversity of AlbertaMcMaster University
FundersCanadian Institutes of Health ResearchPopulation Health Research InstituteUniversity of AlbertaAlberta Innovates - Health SolutionsCanada Research ChairsAlberta InnovatesHeart and Stroke Foundation of Canada
KeywordsMedicineStroke (engine)Internal medicinePost-hoc analysisInfarctionAntithromboticDabigatranMagnetic resonance imagingCardiologySurgeryMyocardial infarctionRadiologyWarfarinAtrial fibrillation

Abstract

fetched live from OpenAlex

OBJECTIVE: To evaluate the relationship between infarct pattern, inferred stroke mechanism and risk of recurrence in patients with ischaemic stroke. The question is clinically relevant to optimise secondary stroke prevention investigations and treatment. DESIGN: We conducted a retrospective analysis of the dabigatran treatment of acute stroke II (DATAS II) trial (ClinicalTrials.gove NCT NCT02295826), in which patients underwent diffusion-weighted imaging (DWI) at baseline and 30 days after randomisation to one of two antithrombotic therapies. Patients were classified as embolic, isolated small subcortical infarcts or transient ischaemic attack TIA (no infarct) at baseline and day 30. Stroke mechanism was determined by traditional and modified (based on DWI lesion findings) Trial of Org 10 172 in Acute Stroke Treatment (TOAST) criteria (DWI-TOAST). SETTING: Multicentre (6) tertiary acute stroke treatment hospitals. PARTICIPANTS: 305 adults with minor ischaemic stroke (National Institutes of Health Stroke Scale (NIHSS) score≤9). RESULTS: Of 305 patients, 148 had embolic pattern infarcts, 93 were isolated small subcortical infarcts and 64 had no infarct on baseline MRI (TIA). In the absence of DWI, TOAST classification indicated the mechanism was cryptogenic in 147 patients (48.2%), and small-vessel occlusion in 127 (41.6%). Using, DWI-TOAST, the number of cryptogenic strokes decreased to 123 (40.3%), and the number of small-vessel occlusion strokes increased to 151 (49.5%). Recurrent infarcts were seen in 13% of patients with an MRI-defined embolic infarct pattern and cryptogenic mechanism on DWI-TOAST. The relative risk of recurrent infarction in patients with undetermined aetiology was increased compared with other categories (standardised coefficient=1.0 (0.1, 1.9), p=0.029). The topography of recurrent infarcts was most often embolic (60.9%), but in 39.1% an isolated small subcortical infarct was seen. CONCLUSIONS: Definitive identification of infarct topography with DWI has a significant impact on infarct mechanism classification. The variable relationship between baseline infarct patterns, clinical presentation and recurrent infarct distribution is a challenge to both the lacunar and embolic stroke of uncertain source (ESUS) concepts. Irrespective of aetiological classification, patients with MRI-defined cryptogenic embolic pattern infarcts are at high risk for recurrent events. TRIAL REGISTRATION NUMBER: Linked to the DATAS II trial. CLINICALTRIALS: gov ID NCT02295826.

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)0.008
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.027
GPT teacher head0.364
Teacher spread0.337 · 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 designNon-randomized trial
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

Citations1
Published2025
Admission routes2
Has abstractyes

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