Evidence map›Paper›PMID 37853392›Full record

ArticleJournal of neuroengineering and rehabilitation2023

Motor-cognitive functions required for driving in post-stroke individuals identified via machine-learning analysis.

Genta Tabuchi, Akira Furui, Seiji Hama, Akiko Yanagawa, Koji Shimonaga, Ziqiang Xu, Zu Soh, Harutoyo Hirano, Toshio Tsuji

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of neuroengineering and rehabilitation, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
2.9field-weighted citation impact, top 9% of its field
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

2 citing papers in PubMed, 4 citations in OpenAlex.

  1. Trial
  2. 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

9 authors at 3 institutions in 1 country.

Genta TabuchiGraduate School of Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-Hiroshima, Hiroshima, 739-8527, Japan.
Akira FuruiGraduate School of Advanced Science and Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-Hiroshima, Hiroshima, 739-8527, Japan.
Seiji HamaGraduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, Hiroshima, 734-8551, Japan. shama@hiroshima-u.ac.jp.
Akiko YanagawaDepartment of Rehabilitation, Hibino Hospital, 7-9-2 Tomo-Higashi, Asaminami-ku, Hiroshima, Hiroshima, 731-3164, Japan.
Koji ShimonagaDepartment of Neurosurgery and Interventional Neuroradiology, Hiroshima City North Medical Center Asa Citizens Hospital, 1-2-1 Kameyamaminami, Asakita-ku, Hiroshima, Hiroshima, 731-0293, Japan.
Ziqiang XuGraduate School of Advanced Science and Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-Hiroshima, Hiroshima, 739-8527, Japan.
Zu SohGraduate School of Advanced Science and Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-Hiroshima, Hiroshima, 739-8527, Japan.
Harutoyo HiranoDepartment of Medical Equipment Engineering, Clinical Collaboration Unit, School of Medical Sciences, Fujita Health University, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan.
Toshio TsujiGraduate School of Advanced Science and Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-Hiroshima, Hiroshima, 739-8527, Japan. tsuji-c@bsys.hiroshima-u.ac.jp.
Hiroshima University · JPFujita Health University Hospital · JPNorth Medical Center · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPeople who were previously hospitalised with stroke may have difficulty operating a motor vehicle, and their driving aptitude needs to be evaluated to prevent traffic accidents in today's car-based society. Although the association between motor-cognitive functions and driving aptitude has been extensively studied, motor-cognitive functions required for driving have not been elucidated.

methodsIn this paper, we propose a machine-learning algorithm that introduces sparse regularization to automatically select driving aptitude-related indices from 65 input indices obtained from 10 tests of motor-cognitive function conducted on 55 participants with stroke. Indices related to driving aptitude and their required tests can be identified based on the output probability of the presence or absence of driving aptitude to provide evidence for identifying subjects who must undergo the on-road driving test. We also analyzed the importance of the indices of motor-cognitive function tests in evaluating driving aptitude to further clarify the relationship between motor-cognitive function and driving aptitude.

resultsThe experimental results showed that the proposed method achieved predictive evaluation of the presence or absence of driving aptitude with high accuracy (area under curve 0.946) and identified a group of indices of motor-cognitive function tests that are strongly related to driving aptitude.

conclusionsThe proposed method is able to effectively and accurately unravel driving-related motor-cognitive functions from a panoply of test results, allowing for autonomous evaluation of driving aptitude in post-stroke individuals. This has the potential to reduce the number of screening tests required and the corresponding clinical workload, further improving personal and public safety and the quality of life of individuals with stroke.

Indexed as

Automobile DrivingStrokeAccidents, TrafficCognitionHumansMachine LearningQuality of LifeDriving aptitudeMachine-learning methodMotor-cognitive functionsPost-stroke individuals

Identifiers

PMID37853392
PMCPMC10583407
OpenAlexW4387739905

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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 OpenQuestion graph.