MétaCan
Menu
← Back to cohort
Record W2760179282 · doi:10.1161/str.44.suppl_1.atp60

Abstract TP60: Intravenous Thrombolysis For Patients With Reverse MRA-DWI Mismatch: SAMURAI And NCVC Rt-PA Registries

2013· article· en· W2760179282 on OpenAlexaboutno aff
Yuki Sakamoto, Masatoshi Koga, Kazumi Kimura, Kazuyuki Nagatsuka, Satoshi Okuda, Kazuomi Kario, Yasuhiro Hasegawa, Yasushi Okada, Hiroshi Yamagami, Eisuke Furui, Jyoji Nakagawara, Yoshiaki Shiokawa, Takuya Okata, Junpei Kobayashi, Eijirou Tanaka, Kazuo Minematsu, Ḱazunori Toyoda

Bibliographic record

VenueStroke · 2013
Typearticle
Languageen
FieldMedicine
TopicCerebrovascular and Carotid Artery Diseases
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineThrombolysisLogistic regressionStroke (engine)Internal medicineOcclusionSingle CenterNuclear medicineSurgeryMyocardial infarction

Abstract

fetched live from OpenAlex

Background and purpose: Characteristics of reverse MRA-DWI mismatch, defined as large DWI lesion despite absence of the major artery occlusion (MAO), remain unknown, especially in patients treated with IV rt-PA. This study aimed to clarify the frequency, associated factors, and outcomes of patients showing reverse MRA-DWI mismatch prior to IV rt-PA therapy. Methods: From the multicenter (SAMURAI) and additional single-center (NCVC) rt-PA registries, patients with the MCA territorial stroke were included. Early ischemic changes (EIC) were assessed with the Alberta Stroke Program Early CT score (ASPECTS) on pretreatment DWI. MAO was defined as ICA or M1 occlusion on MRA. Patients were divided into 4 groups: the large-EIC match (LM) group (MAO, ASPECTS <7); the reverse mismatch (RMM) group (no MAO, ASPECTS <7); the conventional mismatch (CMM) group (MAO, ASPECTS ≧7); and the small-EIC match (SM) group (no MAO, ASPECTS ≧7). Outcomes included sICH per ECASS II criteria, and mRS 0-2 and death at 90 days. Multivariate backward stepwise logistic regression analysis was performed to identify independent clinical characteristics (demographic factors, risk factors, stroke subtypes by TOAST classification, and blood tests) associated with the reverse MRA-DWI mismatch and to compare the outcomes among the 4 groups. Results: Of the 486 patients (167 women, median age 74 years) enrolled, reverse MRA-DWI mismatch was observed in 24 (5%, RMM group); 108 belonged to LM, 161 to CMM, and 193 to SM groups. Among clinical characteristics, cardioembolism (RMM 92%, LM 76%, CM 69%, SM 49%) was only independently associated with the RMM group (OR 5.49, 95%CI 1.25-24.1). Median initial NIHSS score was 18 in RMM, 18 in LM, 13 in CMM, and 8 in SM (p<0.001). MRS 0-2 (RMM 54%, LM 19%, CMM 46%, SM 69%) was more common in the RMM than the LM group (OR 4.02, 95% CI 1.28-12.7). SICH (RMM 13%, LM 6%, CMM 2%, SM 2%) and death (RMM 8%, LM 12%, CMM 9%, SM 2%) were not different between the RMM and LM groups after multivariate analysis. Conclusion: Reverse MRA-DWI mismatch was observed in 5% of patients eligible for rt-PA. Cardioembolism was independently associated with reverse mismatch. Patients with reverse mismatch may benefit from thrombolysis, compared to those with extensive EIC with MAO.

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)0.005
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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.210
Teacher spread0.203 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueStroke→Same topicCerebrovascular and Carotid Artery Diseases→French-language works237,207→