SynthesisPloS one2023
Machine learning in predicting outcomes for stroke patients following rehabilitation treatment: A systematic review.
Synthesis in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses 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
17 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Diagnostic Accuracy of Machine Learning Models in Predicting Functional Outcome of Thrombectomy for Acute Posterior Circulation Artery Occlusion: a Systematic Review and Meta-Analysis.Clinical neuroradiology · 2026Pooled it
- Evaluating predictive performance, validity, and applicability of machine learning models for predicting HIV treatment interruption: a systematic review.BMC global and public health · 2025Pooled it
- Artificial intelligence for gait and balance in neurological disorders: a scoping review of clinical applications and technologies.Journal of neurology · 2026Article
- Artificial Intelligence in Stroke Rehabilitation: A 20-Year Bibliometric Analysis of Digital Health Trends and Technologies.Brain and behavior · 2026Review
- Ten Recommendations for Improving Research on Stroke Motor Rehabilitation: A Unified Perspective.Restorative neurology and neuroscience · 2026Review
- Article
- Article
- Development and internal validation of an artificial intelligence-driven precision rehabilitation model for ischemic stroke: a retrospective cohort study.Frontiers in medicine · 2026Article
- Predicting response to non-invasive brain stimulation in post-stroke upper extremity motor impairment: the importance of neurophysiological and clinical biomarkers.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2025Article
- Prediction of the functional outcome of intensive inpatient rehabilitation after stroke using machine learning methods.Scientific reports · 2025Article
- Machine Learning Predictive Models for Survival in Patients with Brain Stroke.Health promotion perspectives · 2025Article
- Interpretable machine learning-based predictive model for malnutrition in subacute post-stroke patients: an internal and external validation study.Frontiers in nutrition · 2025Article
- Artificial intelligence in stroke risk assessment and management via retinal imaging.Frontiers in computational neuroscience · 2025Review
- Sex-specific infarct volume associations and early prediction of language impairment progression following stroke surgery: a network approach.Frontiers in neurology · 2025Article
- Article
- Mitigating Algorithmic Bias in AI-Driven Cardiovascular Imaging for Fairer Diagnostics.Diagnostics (Basel, Switzerland) · 2024Article
- Association of acupuncture timing and treatment intensity with short-term functional recovery in stroke rehabilitation inpatients: a retrospective cohort and prediction study.Frontiers in neurologyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis review aimed to summarize the use of machine learning for predicting the potential benefits of stroke rehabilitation treatments, to evaluate the risk of bias of predictive models, and to provide recommendations for future models. MATERIALS AND
methodsThis systematic review was conducted in accordance with the PRISMA statement and the CHARMS checklist. The PubMed, Embase, Cochrane Library, Scopus, and CNKI databases were searched up to April 08, 2023. The PROBAST tool was used to assess the risk of bias of the included models.
resultsTen studies within 32 models met our inclusion criteria. The optimal AUC value of the included models ranged from 0.63 to 0.91, and the optimal R2 value ranged from 0.64 to 0.91. All of the included models were rated as having a high or unclear risk of bias, and most of them were downgraded due to inappropriate data sources or analysis processes. DISCUSSION AND
conclusionThere remains much room for improvement in future modeling studies, such as high-quality data sources and model analysis. Reliable predictive models should be developed to improve the efficacy of rehabilitation treatment by clinicians.
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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.