Identification of ischemic stroke subtypes defined by inflammation, coagulation, and metabolic profiles.

Hezhen Gao, Dilraba Mahmut, Fanshu Dai, Haimiao Yu, Wei Chang, Xingya Huang, Biao Zhang

Abstract read

In one paragraph

Article in Frontiers in artificial intelligence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

Cumulative inflammatory, coagulation, and metabolic abnormalities predict poor 90-day functional outcome after endovascular thrombectomy for large-vessel occlusion acute ischemic stroke.Frontiers in neurology · 2026
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Hezhen Gao *Tianjin Key Laboratory of Cerebral Vascular and Neurodegenerative Diseases, Department of Clinical Laboratory, Huanhu Hospital Affiliated to Tianjin Medical University, Tianjin, China.

Dilraba Mahmut *Tianjin Key Laboratory of Cerebral Vascular and Neurodegenerative Diseases, Department of Clinical Laboratory, Huanhu Hospital Affiliated to Tianjin Medical University, Tianjin, China.

Fanshu Dai *Tianjin Key Laboratory of Cerebral Vascular and Neurodegenerative Diseases, Department of Clinical Laboratory, Huanhu Hospital Affiliated to Tianjin Medical University, Tianjin, China.

Haimiao YuTianjin Key Laboratory of Cerebral Vascular and Neurodegenerative Diseases, Department of Clinical Laboratory, Huanhu Hospital Affiliated to Tianjin Medical University, Tianjin, China.

Wei ChangTianjin Key Laboratory of Cerebral Vascular and Neurodegenerative Diseases, Department of Clinical Laboratory, Huanhu Hospital Affiliated to Tianjin Medical University, Tianjin, China.

Xingya HuangTianjin Key Laboratory of Cerebral Vascular and Neurodegenerative Diseases, Department of Clinical Laboratory, Huanhu Hospital Affiliated to Tianjin Medical University, Tianjin, China.

Biao ZhangTianjin Key Laboratory of Cerebral Vascular and Neurodegenerative Diseases, Department of Clinical Laboratory, Huanhu Hospital Affiliated to Tianjin Medical University, Tianjin, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Ischemic stroke is a heterogeneous disease influenced by inflammation, coagulation dysfunction, and metabolic disturbances. However, integrated analysis incorporating these biological domains for patient stratification remain limited. Methods: A retrospective study of 132 ischemic stroke patients was conducted. Clinical, coagulation, inflammatory, and metabolic parameters were collected. Principal component analysis (PCA) was applied for dimensionality reduction and visualization. Results: PCA revealed underlying heterogeneity among patients. Validated Conclusion: Data driven clustering identified biologically distinct ischemic stroke subtypes based on inflammation, coagulation, and metabolic profiles. This stratification highlights the heterogeneity of ischemic stroke and may inform future personalized approaches to risk assessment and management.

Indexed as

clustering analysiscoagulationinflammationischemic strokemetabolic profiles

Identifiers

PMID41883754

DOI10.3389/frai.2026.1776891

PMCPMC13008919

What Socratic holds

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.