ArticlePhysical therapy2019
Predicting Improved Daily Use of the More Affected Arm Poststroke Following Constraint-Induced Movement Therapy.
Article in Physical therapy, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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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.
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.
Who cites it
9 citing papers in PubMed, 45 citations in OpenAlex.
- Using mechanistic knowledge to appraise contemporary approaches to the rehabilitation of upper limb function following stroke.The Journal of physiology · 2025Review
- Somatosensory-Evoked Potentials and Clinical Assessments of Sensory Function Over Time in Patients With Subacute Stroke.Neural plasticity · 2025Article
- Self-Supervised Learning for Near-Wild Cognitive Workload Estimation.Journal of medical systems · 2024Article
- AI Applications in Adult Stroke Recovery and Rehabilitation: A Scoping Review Using AI.Sensors (Basel, Switzerland) · 2024Article
- Predicting Outcome in Patients with Brain Injury: Differences between Machine Learning versus Conventional Statistics.Biomedicines · 2022Review
- Do somatosensory deficits predict efficacy of neurorehabilitation using neuromuscular electrical stimulation for moderate to severe motor paralysis of the upper limb in chronic stroke?Therapeutic advances in neurological disorders · 2021Article
- 3D Motion Capture May Detect Spatiotemporal Changes in Pre-Reaching Upper Extremity Movements with and without a Real-Time Constraint Condition in Infants with Perinatal Stroke and Cerebral Palsy: A Longitudinal Case Series.Sensors (Basel, Switzerland) · 2020Article
- Predictors of Arm Nonuse in Chronic Stroke: A Preliminary Investigation.Neurorehabilitation and neural repair · 2020Article
- Validity and Reliability of the Semmes-Weinstein Monofilament Test and the Thumb Localizing Test in Patients With Stroke.Frontiers in neurology · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 4 institutions in 1 country.
Funding
Abstract
backgroundConstraint-induced movement therapy (CI therapy) produces, on average, large and clinically meaningful improvements in the daily use of a more affected upper extremity in individuals with hemiparesis. However, individual responses vary widely.
objectiveThe study objective was to investigate the extent to which individual characteristics before treatment predict improved use of the more affected arm following CI therapy.
designThis study was a retrospective analysis of 47 people who had chronic (> 6 months) mild to moderate upper extremity hemiparesis and were consecutively enrolled in 2 CI therapy randomized controlled trials.
methodsAn enhanced probabilistic neural network model predicted whether individuals showed a low, medium, or high response to CI therapy, as measured with the Motor Activity Log, on the basis of the following baseline assessments: Wolf Motor Function Test, Semmes-Weinstein Monofilament Test of touch threshold, Motor Activity Log, and Montreal Cognitive Assessment. Then, a neural dynamic classification algorithm was applied to improve prognostic accuracy using the most accurate combination obtained in the previous step.
resultsMotor ability and tactile sense predicted improvement in arm use for daily activities following intensive upper extremity rehabilitation with an accuracy of nearly 100%. Complex patterns of interaction among these predictors were observed. LIMITATIONS: The fact that this study was a retrospective analysis with a moderate sample size was a limitation.
conclusionsAdvanced machine learning/classification algorithms produce more accurate personalized predictions of rehabilitation outcomes than commonly used general linear models.
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Registered trials
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.