ArticleBrain sciences2022
Predicting Long-Term Recovery of Consciousness in Prolonged Disorders of Consciousness Based on Coma Recovery Scale-Revised Subscores: Validation of a Machine Learning-Based Prognostic Index.
Article in Brain sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05819177 (An Analysis of the Information of the Coma Recovery Scale-Revised Providing the Most Reliable Prediction of the Neurobehavioral State and Recovery of Consciousness), which is not on this map. Cited by 10 papers, 3 of them syntheses that pooled it.
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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.
An Analysis of the Information of the Coma Recovery Scale-Revised Providing the Most Reliable Prediction of the Neurobehavioral State and Recovery of Consciousness
Who cites it
10 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Prediction models for functional outcomes in prolonged disorders of consciousness: a systematic review and meta-analysis.Journal of neuroengineering and rehabilitation · 2025Pooled it
- Care pathways for individuals with post-anoxic disorder of consciousness (CaPIADoC): an inter-society Consensus Conference.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2025Pooled it
- The Neurological Rehabilitation of Adults With Coma and Disorders of Consciousness.Deutsches Arzteblatt international · 2023Guideline
- Artificial intelligence could reshape research and care in disorders of consciousness.Nature reviews. Neurology · 2026Article
- Predictors of Recovering Full Consciousness: Results From a Prospective Multisite Italian Study.European journal of neurology · 2025Observational
- Can artificial intelligence improve the diagnosis and prognosis of disorders of consciousness? A scoping review.Frontiers in artificial intelligence · 2025Review
- Review
- Motion acquisition of gait characteristics one week after total hip arthroplasty: a factor analysis.Archives of orthopaedic and trauma surgery · 2024Article
- Which information derived from the Coma Recovery Scale-Revised provides the most reliable prediction of clinical diagnosis and recovery of consciousness? A comparative study using machine learning techniques.European journal of physical and rehabilitation medicine · 2024Article
- Prognosis of patients with prolonged disorders of consciousness after brain injury: a longitudinal cohort study.Frontiers in public health · 2024Article
Corrections and comments
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Authors and funding
20 authors.
Funding
Abstract
Prognosis of prolonged Disorders of Consciousness (pDoC) is influenced by patients' clinical diagnosis and Coma Recovery Scale-Revised (CRS-R) total score. We compared the prognostic accuracy of a novel Consciousness Domain Index (CDI) with that of clinical diagnosis and CRS-R total score, for recovery of full consciousness at 6-, 12-, and 24-months post-injury. The CDI was obtained by a combination of the six CRS-R subscales via an unsupervised machine learning technique. We retrospectively analyzed data on 143 patients with pDoC (75 in Minimally Conscious State; 102 males; median age = 53 years; IQR = 35; time post-injury = 1-3 months) due to different etiologies enrolled in an International Brain Injury Association Disorders of Consciousness Special Interest Group (IBIA DoC-SIG) multicenter longitudinal study. Univariate and multivariate analyses were utilized to assess the association between outcomes and the CDI, compared to clinical diagnosis and CRS-R. The CDI, the clinical diagnosis, and the CRS-R total score were significantly associated with a good outcome at 6, 12 and 24 months. The CDI showed the highest univariate prediction accuracy and sensitivity, and regression models including the CDI provided the highest values of explained variance. A combined scoring system of the CRS-R subscales by unsupervised machine learning may improve clinical ability to predict recovery of consciousness in patients with pDoC.
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