Trial reportPloS one2018
Regression techniques employing feature selection to predict clinical outcomes in stroke.
Trial report in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled 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.
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
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning methods for functional recovery prediction and prognosis in post-stroke rehabilitation: a systematic review.Journal of neuroengineering and rehabilitation · 2022Pooled it
- Effects of robot viscous forces on arm movements in chronic stroke survivors: a randomized crossover study.Journal of neuroengineering and rehabilitation · 2020Trial
- Key components of mechanical work predict outcomes in robotic stroke therapy.Journal of neuroengineering and rehabilitation · 2020Trial
- Exploration and analysis of risk factors for coronary artery disease with type 2 diabetes based on SHAP explainable machine learning algorithm.Scientific reports · 2025Article
- Machine learning techniques for independent gait recovery prediction in acute anterior circulation ischemic stroke.Journal of neuroengineering and rehabilitation · 2025Article
- Predicting upper limb motor recovery in subacute stroke patients via fNIRS-measured cerebral functional responses induced by robotic training.Journal of neuroengineering and rehabilitation · 2024Article
- Hip and Knee Joint Kinematics Predict Quadriceps Hyperreflexia in People with Post-stroke Stiff-Knee Gait.Annals of biomedical engineering · 2023Article
- Discovering domains important to health-related quality of life in differentiated thyroid cancer.American journal of surgery · 2022Article
- Estimating upper-extremity function from kinematics in stroke patients following goal-oriented computer-based training.Journal of neuroengineering and rehabilitation · 2021Article
- Predicting clinically significant motor function improvement after contemporary task-oriented interventions using machine learning approaches.Journal of neuroengineering and rehabilitation · 2020Article
- Inpatient stroke rehabilitation: prediction of clinical outcomes using a machine-learning approach.Journal of neuroengineering and rehabilitation · 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
3 authors.
Funding
Abstract
It is not fully clear which measurable factors can reliably predict chronic stroke patients' recovery of motor ability. In this analysis, we investigate the impact of patient demographic characteristics, movement features, and a three-week upper-extremity intervention on the post-treatment change in two widely used clinical outcomes-the Upper Extremity portion of the Fugl-Meyer and the Wolf Motor Function Test. Models based on LASSO, which in validation tests account for 65% and 86% of the variability in Fugl-Meyer and Wolf, respectively, were used to identify the set of salient demographic and movement features. We found that age, affected limb, and several measures describing the patient's ability to efficiently direct motions with a single burst of speed were the most consequential in predicting clinical recovery. On the other hand, the upper-extremity intervention was not a significant predictor of recovery. Beyond a simple prognostic tool, these results suggest that focusing therapy on the more important features is likely to improve recovery. Such validation-intensive methods are a novel approach to determining the relative importance of patient-specific metrics and may help guide the design of customized therapy.
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What OpenQuestion holds
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.