Evidence map›Paper›PMID 42523428›Full record

ArticlemedRxiv : the preprint server for health sciences2026

Distinct Patterns of Mobility Recovery After Stroke Using Routine Clinical Data.

Margaret A French, Elizabeth B Marsh, Ryan Roemmich, Preeti Raghavan

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In one paragraph

Article in medRxiv : the preprint server for health sciences, 2026. 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

4 authors.

Margaret A FrenchDepartment of Physical Therapy and Athletic Training, University of Utah, Salt Lake City, UT, USA.ORCID 0000-0002-3581-7374
Elizabeth B MarshDivision of Stroke and Vascular Neurology, Department of Neurology, Johns Hopkins Hospital, Baltimore, MD, USA.ORCID 0000-0002-2385-5074
Ryan RoemmichDepartment of Physical Medicine and Rehabilitation, Johns Hopkins Hospital, Baltimore, MD, USA.ORCID 0000-0003-0797-6455
Preeti RaghavanDepartment of Physical Medicine and Rehabilitation, Johns Hopkins Hospital, Baltimore, MD, USA.ORCID 0000-0002-7852-5005

Funding

Identifying and predicting subgroups related to function in individuals after stroke.F32HD108835 · NICHD · JOHNS HOPKINS UNIVERSITY · PI FRENCH, MARGARET · 2022 to 2022
$59k
NICHD NIH HHS F32 HD108835
6 · The paper itself

Abstract

Background: Mobility recovery after stroke is highly variable, yet is typically described using average patterns that obscure meaningful differences between individuals. Identifying distinct recovery trajectories may improve prognostication and guide rehabilitation strategies. Methods: We conducted a retrospective cohort study of adults admitted for stroke to a large health system between 2016 and 2024. Mobility was assessed using Activity Measure for Post-Acute Care (AM-PAC) Basic Mobility, which was collected during routine clinical care. Growth mixture modeling was used to identify subgroups with distinct mobility recovery trajectories during the first 180 days after stroke. Subgroups were then characterized with baseline personal and clinical characteristics. Results: Seven hundred and fifty individuals contributed 3,389 mobility observations (median 4 per person). A five-class solution was selected based on model fit and classification quality. Distinct trajectories were identified: low stable (n=127), low rapidly improving (n=29), mid declining (n=169), mid improving (n=365), and high stable (n=60). Subgroups differed in both baseline mobility and patterns of change over time, with some demonstrating improvement, others remaining stable, and one declining. Individuals in improving subgroups were generally younger, more likely to be independent before stroke, received physical therapy on a greater proportion of hospital days, and were more frequently discharged to inpatient rehabilitation. In contrast, those in low or declining trajectories had lower baseline function, longer hospital stays, and were more likely to be discharged to skilled nursing facilities. Conclusions: The distinct mobility recovery trajectories identified in this work reflect the heterogeneity present in routine clinical practice. Subgroups differed in both recovery patterns and characteristics. Early identification of trajectory membership may improve prognostication and inform more targeted rehabilitation strategies.

Indexed as

Electronic health recordsMobilityRecovery of functionStrokeTrajectory analysis

Identifiers

PMID42523428
PMCPMC13405380

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.