Evidence map›Paper›PMID 35932111›Full record

ArticleNeurorehabilitation and neural repair2023

Assessment of Neurological Impairment and Recovery Using Statistical Models of Neurologically Healthy Behavior.

Stephen H Scott, Catherine R Lowrey, Ian E Brown, Sean P Dukelow

Abstract read
In one paragraph

Article in Neurorehabilitation and neural repair, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

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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.

Stephen H ScottCentre for Neuroscience Studies, Queen's University, Kingston, ON, Canada.
Catherine R LowreyCentre for Neuroscience Studies, Queen's University, Kingston, ON, Canada.ORCID 0000-0002-8379-9924
Ian E BrownKinarm, BKIN Technologies Ltd. Kingston, ON, Canada.
Sean P DukelowHotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

While many areas of medicine have benefited from the development of objective assessment tools and biomarkers, there have been comparatively few improvements in techniques used to assess brain function and dysfunction. Brain functions such as perception, cognition, and motor control are commonly measured using criteria-based, ordinal scales which can be coarse, have floor/ceiling effects, and often lack the precision to detect change. There is growing recognition that kinematic and kinetic-based measures are needed to quantify impairments following neurological injury such as stroke, in particular for clinical research and clinical trials. This paper will first consider the challenges with using criteria-based ordinal scales to quantify impairment and recovery. We then describe how kinematic-based measures can overcome many of these challenges and highlight a statistical approach to quantify kinematic measures of behavior based on performance of neurologically healthy individuals. We illustrate this approach with a visually-guided reaching task to highlight measures of impairment for individuals following stroke. Finally, there has been considerable controversy about the calculation of motor recovery following stroke. Here, we highlight how our statistical-based approach can provide an effective estimate of impairment and recovery.

Indexed as

StrokeStroke RehabilitationBiomechanical PhenomenaHealth BehaviorHumansModels, StatisticalRecovery of FunctionUpper Extremityimpairmentkinematicsrecoveryrobotsstroke assessment

Identifiers

PMID35932111
PMCPMC10315872

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

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 OpenQuestion graph.