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Analysis on prognostic factors of ischemic stroke caused by middle cerebral artery stenosis or occlusion

2018· article· en· W6910391430 on OpenAlexaboutno aff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2018
Typearticle
Languageen
FieldNeuroscience
TopicNeurological Disease Mechanisms and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsStroke (engine)StenosisReceiver operating characteristicBlood pressureLogistic regressionMiddle cerebral arteryOcclusionUnivariate analysis

Abstract

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Objective To screen related influencing factors for good prognosis of ischemic stroke caused by middle cerebral artery (MCA) stenosis or occlusion. Methods A total of 109 patients with ischemic stroke caused by MCA stenosis or occlusion were enrolled in this study. Their clinical data were recorded, such as sex, age, time from onset to MRI, history [stroke and/or transient ischemic attack (TIA), coronary heart disease, atrial fibrillation, smoking and drinking], systolic blood pressure (SBP), diastolic blood pressure (DBP), serum glucose, total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), plasma homocysteine (Hcy), National Institutes of Health Stroke Scale (NIHSS) score on admission and discharge, modified Rankin Scale (mRS) at 3-month follow-up. MRI and three-dimensional time-of-flight (3D-TOF) MRA were performed to get MCA stenosis rate, FLAIR vascular hyperintensity (FVH) score, FVH-diffusion-weighted imaging (DWI) mismatching proportion, DWI-Alberta Stroke Program Early CT Score (ASPECTS). Univariate and multivariate forward Logistic regression analysis was used to screen related influencing factors for good prognosis. Receiver operating characteristic (ROC) curve was used to evaluate the accuracy and best threshold of FVH and DWI-ASPECTS scores predicting good prognosis. Results According to mRS score, patients were divided into good prognosis group (mRS ? 2, N = 24) and poor prognosis group (mRS > 2, N = 85). Logistic regression analysis showed that DBP (OR = 0.924, 95% CI: 0.869-0.983; P = 0.013), FVH score (OR = 2.008, 95% CI: 1.404-2.873; P = 0.000) and DWI-ASPECTS score (OR = 1.955, 95% CI: 1.336-2.862; P = 0.001) were independent influencing factors for good prognosis. ROC curve showed that area under the curve (AUC) of FVH score was 0.768 (95% CI: 0.656-0.880, P = 0.000), AUC of DWI-ASPECTS score was 0.721 (95%CI: 0.608-0.834, P = 0.001). The best threshold of FVH score was 4.50, sensitivity was 0.625, specificity was 0.824, Youden index was 0.449. The best threshold of DWI-ASPECTS score was 6.50, sensitivity was 0.750, specificity was 0.671, Youden index was 0.421. Conclusions For ischemic stroke caused by MCA stenosis or occlusion, the related prognostic factors include DBP, FVH score and DWI-ASPECTS score, whereas FVH score has higher accuracy. DOI:10.3969/j.issn.1672-6731.2018.04.003

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)0.000
metaresearch head score (Gemma)0.001
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.261
GPT teacher head0.480
Teacher spread0.219 · 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".

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Citations0
Published2018
Admission routes1
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