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Treatment of MRA-DWI mismatched patients with mild ischemic stroke caused by middle cerebral artery M1 segment occlusion: comparison of endovascular treatment and intravenous thrombolytic therapy

2019· article· en· W3030871421 on OpenAlexaboutno aff
Linming Xun, Zhensheng Liu, Tieyu Tang, Yingge Wang

Bibliographic record

VenueInt J Cerebrovasc Dis · 2019
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineModified Rankin ScaleThrombolysisOcclusionMiddle cerebral arteryStroke (engine)Computed tomography angiographyMagnetic resonance angiographyNeurologyMagnetic resonance imagingRadiologyAngiographyInternal medicineSurgeryIschemiaIschemic strokeMyocardial infarction

Abstract

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Objective To investigate the effect of different treatment regimens guided by magnetic resonance angiography (MRA) and diffusion weighted imaging (DWI) mismatch on the outcomes of patients with mild ischemic stroke caused by acute middle cerebral artery (MCA) M1 segment occlusion. Methods From January 2013 to February 2018, the clinical data of patients with mild ischemic stroke caused by acute MCA M1 segment occlusion and admitted to the Department of Neurology, the Affiliated Hospital of Yangzhou University were analyzed retrospectively. Mild stroke was defined as the National Institutes of Health Stroke Scale (NIHSS) score ≤5, and the MRA-DWI mismatch was defined as MCA M1 segment occlusion confirmed by MRA and the DWI-Alberta Stroke Program Early Computed Tomography Score ≥6. According to the clinical decision, they were divided into endovascular treatment group and intravenous thrombolytic therapy group. The primary outcome measure was the modified Rankin Scale score at 90 days after onset, ≤2 was defined as good outcome. The secondary outcome measure was the incidence of symptomatic intracranial hemorrhage (sICH) within 7 days after treatment and the mortality rate at 90 d. Multivariate logistic regression analysis was used to determine the independent effects of different treatment regimens on outcomes. Results A total of 38 patients were enrolled, 19 (50.00%) in the intravenous thrombolytic therapy group, and 19 in the endovascular treatment group (50.00%, including 5 patients with intratracheal thrombectomy after intravenous thrombolysis); 27 patients had good outcomes (71.05%) and 11 had poor outcomes (28.95%). Except for total cholesterol level, there were no significant differences in demography, vascular risk factors, and all baseline clinical data between the endovascular treatment group and the intravenous thrombolytic therapy group. The rate of good outcome in the endovascular treatment group was significantly higher than that in the intravenous thrombolytic therapy group (89.47% vs. 2.63%; P=0.029), and there was no significant difference between the incidence of sICH within 7 days (15.79% vs. 5.26%; P=0.604) and 90-day mortality (0% vs. 10.53%; P=0.486). The proportion of patients who underwent endovascular treatment in the good outcome group was significantly higher than that in the poor outcome group (62.96% vs. 18.18%; P=0.029). Multivariate logistic regression analysis showed that endovascular treatment was an independent predictor of good outcome (odds ratio 0.103, 95% confidence interval 0.015-0.714; P=0.021). Conclusion Endovascular treatment is an independent predictor of good outcome in patients with mild ischemic stroke caused by acute MCA M1 segment occlusion. Key words: Stroke; Brain ischemia; Middle cerebral artery; Severity of illness index; Magnetic resonance imaging; Magnetic resonance angiography; Thrombectomy; Endovascular procedures; Thrombolytic therapy; Tissue plasminogen activator; Treatment outcome

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.714
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.015
GPT teacher head0.237
Teacher spread0.222 · 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 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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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