Evidence map›Paper›PMID 40808920›Full record

ReviewFrontiers in neurology2025

Measuring severe stroke: a scoping review of RCTs.

Katrin Roesner, Hanna Brodowski, Nicole Strutz

Abstract readReview
In one paragraph

Review in Frontiers in neurology, 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

3 authors.

Katrin RoesnerDepartment of Physiotherapy, Pain and Exercise Research Luebeck, Institute of Health Sciences, Universität zu Lübeck, Lübeck, Germany.
Hanna BrodowskiDepartment of Physiotherapy, Pain and Exercise Research Luebeck, Institute of Health Sciences, Universität zu Lübeck, Lübeck, Germany.
Nicole StrutzDepartment of Orthopedic and Trauma Surgery, Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Stroke severity affects length of hospital stay and functional recovery in rehabilitation. Therefore, establishing baseline data of stroke severity is a crucial step. In 2017, neurorehabilitation researchers met at the Stroke Recovery and Rehabilitation Roundtable (SRRR) to build a consensus on new standards for stroke recovery research. Core outcomes for measurement in stroke trials resulted in the recommendation that severe stroke should be assessed using the NIHSS. This scoping review aims to provide an overview of the variety of measurements used in clinical research to assess severe stroke. Methods: RCTs and CCTs were identified by searching PubMed, CENTRAL, SSCI, and ICTRP, covering articles published between January 2018 and September 2024. Peer-reviewed articles in English focusing on rehabilitative interventions and patients aged 18 years or older who have been classified with a severe stroke. The articles included were analyzed according to used measurements and cut-off scores. Results: The initial search yielded 1,004 publications, of which 35 (3.6%) studies were deemed eligible. In total, 11 different measures were used to assess severe stroke. Most studies used the NIHSS ( Conclusion: This review indicates substantial variability in measurements and a diverse range of cut-off scores. Consequently, comparability of patients' baseline stroke severity across studies is limited. Given the fact that the NIHSS is only partially used, future efforts should focus on barriers and challenges using the NIHSS.

Indexed as

cut-off scoresneurological rehabilitationNIHSSoutcome measurestroke phasestroke severity

Identifiers

PMID40808920
PMCPMC12344423

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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.