ReviewConcussion (London, England)2017
Big Data in traumatic brain injury; promise and challenges.
Review in Concussion (London, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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
16 citing papers in PubMed.
- Mapping concussion-induced cerebellar injury: a personalized approach using diffusion tensor imaging (DTI).Magma (New York, N.Y.) · 2026Article
- Scoping review of secondary studies using the Federal Interagency of Traumatic Brain Injury Research datasets.Brain injury · 2026Article
- Revisiting the critical roles of reactive microglia in traumatic brain injury.International journal of surgery (London, England) · 2025Review
- Traumatic Brain Injury and Artificial Intelligence: Shaping the Future of Neurorehabilitation-A Review.Life (Basel, Switzerland) · 2025Review
- Temporal characterisation and electrophysiological implications of TBI-induced serine/threonine kinase activity in mouse cortex.Cellular and molecular life sciences : CMLS · 2025Article
- Increasing Rigor of Preclinical Research to Maximize Opportunities for Translation.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2023Review
- The evolution of Big Data in neuroscience and neurology.Journal of big data · 2023Article
- A machine learning model for predicting favorable outcome in severe traumatic brain injury patients after 6 months.Acute and critical care · 2022Article
- Empowering Data Sharing and Analytics through the Open Data Commons for Traumatic Brain Injury Research.Neurotrauma reports · 2022Article
- Evaluating Performance of EEG Data-Driven Machine Learning for Traumatic Brain Injury Classification.IEEE transactions on bio-medical engineering · 2021Article
- Post-traumatic Headache and Mild Traumatic Brain Injury: Brain Networks and Connectivity.Current pain and headache reports · 2021Review
- A network-based response feature matrix as a brain injury metric.Biomechanics and modeling in mechanobiology · 2020Article
- Big Data Analytics and Sensor-Enhanced Activity Management to Improve Effectiveness and Efficiency of Outpatient Medical Rehabilitation.International journal of environmental research and public health · 2020Review
- Race and Concussion: An Emerging Relationship.Ochsner journal · 2020Article
- How to Translate Time: The Temporal Aspects of Rodent and Human Pathobiological Processes in Traumatic Brain Injury.Journal of neurotrauma · 2019Review
- Harmonizing the Federal Interagency Traumatic Brain Injury Research Informatics System to Study Pre-Injury Immunomodulator Exposure: A Proof-of-Concept Analysis.Neurotrauma reportsArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Traumatic brain injury (TBI) is a spectrum disease of overwhelming complexity, the research of which generates enormous amounts of structured, semi-structured and unstructured data. This resulting big data has tremendous potential to be mined for valuable information regarding the "most complex disease of the most complex organ". Big data analyses require specialized big data analytics applications, machine learning and artificial intelligence platforms to reveal associations, trends, correlations and patterns not otherwise realized by current analytical approaches. The intersection of potential data sources between experimental TBI and clinical TBI research presents inherent challenges for setting parameters for the generation of common data elements and to mine existing legacy data that would allow highly translatable big data analyses. In order to successfully utilize big data analyses in TBI, we must be willing to accept the messiness of data, collect and store all data and give up causation for correlation. In this context, coupling the big data approach to established clinical and pre-clinical data sources will transform current practices for triage, diagnosis, treatment and prognosis into highly integrated evidence-based patient care.
Indexed as
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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.