Evidence map›Paper›PMID 41111968›Full record

ArticleFrontiers in neurology2025

The heterogeneous life space trajectories and predictors in stroke patients: a cohort study.

Bei Yang, Rui Xie, Siyuan Ge, Ruixue Tang, Kangyao Cheng, Yin Wang

Abstract read
In one paragraph

Article in Frontiers in neurology, 2025. 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.

  1. 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

6 authors.

Bei Yang *Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Rui Xie *School of Nursing, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Siyuan GeSchool of Nursing, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Ruixue TangSchool of Nursing, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Kangyao ChengSchool of Nursing, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Yin WangSchool of Nursing, Shanghai University of Traditional Chinese Medicine, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: This study aimed to identify heterogeneous trajectories of life space among stroke patients and explore the predictors for different classes of life space. Methods: This prospective cohort study assessed 210 stroke patients' life space at baseline and 1, 3, 6 months post-discharge. We elucidated heterogeneous trajectories of life space by latent class growth model and explored the predictors of trajectories by multinomial logistic regression analysis. Results: Among 173 participants completing the 6-month follow-up, three distinct life space trajectories were identified: the "high-level recovery flat class" (8%), the "medium-level recovery good class" (72%), and the "low-level recovery poor class" (20%). Multinomial logistic regression, using the low-level recovery poor class as the reference, indicated that age <60, absence of limb sensory deficit, and positive environmental experiences were predictors of the medium-level recovery good class, whereas employment status and positive environmental experiences were predictors of the high-level recovery flat class. Conclusion: The three trajectories of life space indicated that the 1 month post-discharge is the most vulnerable phase for stroke patients. Age and employment status significantly influence life space trajectories. Patients in the low-level recovery poor class should receive special attention. Strategies to improve sensory deficits and environmental experiences should be developed to expand life space, promoting stroke patients' rehabilitation.

Indexed as

latent class growth modellife spacepredictorsstroketrajectories

Identifiers

PMID41111968
PMCPMC12531049

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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 OpenQuestion graph.