Evidence map›Paper›PMID 41365590›Full record

ArticleBMJ open2025

Machine learning-driven health profiling and multidimensional trajectory analysis in first-ever ischaemic stroke: protocol for a multicentre cross-sectional and prospective longitudinal study.

Shu-Lin Li, Jia-Chun You, Qi Wang, Si-Yu Chen, Jiao-Lin Chu, Qing-Xia Li, Rong Chen, Yan-Jin Huang

Abstract readClinical Trial Protocol
In one paragraph

Article in BMJ open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Shu-Lin LiSchool of Nursing, University of South China, Hengyang, China.ORCID http://orcid.org/0009-0003-6485-7230
Jia-Chun YouSchool of Nursing, University of South China, Hengyang, China.ORCID http://orcid.org/0009-0006-0547-4215
Qi WangSchool of Nursing, The Hong Kong Polytechnic University, Hong kong, China.ORCID http://orcid.org/0000-0002-9449-6232
Si-Yu ChenSchool of Nursing, University of South China, Hengyang, China.
Jiao-Lin ChuSchool of Nursing, University of South China, Hengyang, China.
Qing-Xia LiSchool of Nursing, University of South China, Hengyang, China.
Rong ChenThe Nursing Department, The Central Hospital of Shaoyang City, Shaoyang, China huangyanjin@nxmu.edu.cn 13873958316@163.com.
Yan-Jin HuangSchool of Nursing, Ningxia Medical University, China, Yinchuan, China huangyanjin@nxmu.edu.cn 13873958316@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIschaemic stroke, the most prevalent stroke subtype, imposes a significant long-term disease burden. However, patients with first-ever stroke exhibit substantial individual variability in poststroke health trajectories, manifesting heterogeneous clinical presentations. We therefore started with the overall health of patients in order to delineate heterogeneous clusters characterised by distinct demographic profiles, clinical features and behavioural determinants and elucidate shared longitudinal trajectories in the temporal development of adverse health outcomes. METHOD AND ANALYSIS: We designed a multicentre, cross-sectional and longitudinal study focusing on patients with first-ever ischaemic stroke. We will employ patient self-reported outcomes and objective measurements to comprehensively evaluate patients' health status from a multidimensional perspective. Following baseline assessments, participants will undergo follow-up evaluations at 1 month, 3 months and 6 months post inclusion. The primary objective is twofold: (1) to identify distinct patient clusters with heterogeneous multidimensional health profiles using the k-prototype clustering algorithm and (2) to characterise synergistic trajectories of core health attributes within the largest cluster through parallel process latent class growth modelling. By combining cross-sectional and longitudinal analyses, this phased study should elucidate static heterogeneity and dynamic recovery patterns following a first-ever ischaemic stroke. ETHICS AND DISSEMINATION: The project conforms to the ethical principles enshrined in the Declaration of Helsinki (2013 amendment) and all local ethical guidelines. The ethics committee at the University of South China approved the study (approval no. 2024 NHHL023). The ethics committee of Gansu Provincial Hospital approved the study (approval no. 2025-023). The ethics committee of the Central Hospital of Shaoyang approved the study (approval no.KY-2025-12). The findings will be published and presented at conferences for widespread dissemination. TRIAL REGISTRATION NUMBER: ChiCTR2500098442.

Indexed as

Ischemic StrokeMachine LearningChinaCross-Sectional StudiesFemaleHealth StatusHumansLongitudinal StudiesMaleMiddle AgedMulticenter Studies as TopicObservational Studies as TopicProspective StudiesResearch DesignHealthPatientsStroke medicine

Identifiers

PMID41365590
PMCPMC12699551

What Socratic holds

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Registered trials

None linked

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.