ArticleJournal of medical systems2015
Predicting long-term outcome after traumatic brain injury using repeated measurements of Glasgow Coma Scale and data mining methods.
Article in Journal of medical systems, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Meta-analysis of Predicting Disorders of Consciousness After Traumatic Brain Injury by Machine Learning Models.Alpha psychiatry · 2024Pooled it
- Predicting Traumatic Brain Injury Post-Trauma Using Temporal Attention on Sleep-Wake Data.IEEE transactions on bio-medical engineering · 2026Article
- Glasgow Coma Scale Score at Admission in Traumatic Brain Injury Patients: A Multicenter Observational Analysis.Journal of clinical medicine · 2025Article
- Predicting outcomes after moderate and severe traumatic brain injury using artificial intelligence: a systematic review.NPJ digital medicine · 2025Article
- The determinants for death in hospital following moderate to severe traumatic brain injury in Australia.Emergency medicine Australasia : EMA · 2025Article
- Identification and Connectomic Profiling of Concussion Using Bayesian Machine Learning.Journal of neurotrauma · 2024Article
- Prognosis prediction in traumatic brain injury patients using machine learning algorithms.Scientific reports · 2023Article
- Initial CT-based radiomics nomogram for predicting in-hospital mortality in patients with traumatic brain injury: a multicenter development and validation study.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2022Article
- Chinese Admission Warning Strategy for Predicting the Hospital Discharge Outcome in Patients with Traumatic Brain Injury.Journal of clinical medicine · 2022Article
- Mechanism of Ferroptosis and Its Relationships with Other Types of Programmed Cell Death: Insights for Potential Therapeutic Benefits in Traumatic Brain Injury.Oxidative medicine and cellular longevity · 2022Review
- Harnessing repeated measurements of predictor variables for clinical risk prediction: a review of existing methods.Diagnostic and prognostic research · 2020Review
- Prognosis of Six-Month Glasgow Outcome Scale in Severe Traumatic Brain Injury Using Hospital Admission Characteristics, Injury Severity Characteristics, and Physiological Monitoring during the First Day Post-Injury.Journal of neurotrauma · 2019Article
- Use of Diffusional Kurtosis Imaging and Dynamic Contrast-Enhanced MR Imaging to Predict Posttraumatic Epilepsy in Rabbits.AJNR. American journal of neuroradiology · 2018Article
- Accurate and dynamic predictive model for better prediction in medicine and healthcare.Irish journal of medical science · 2018Article
- Diagnosis of Brain Metastases from Lung Cancer Using a Modified Electromagnetism like Mechanism Algorithm.Journal of medical systems · 2016Article
- The application of data mining techniques to oral cancer prognosis.Journal of medical systems · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Previous studies have identified some clinical parameters for predicting long-term functional recovery and mortality after traumatic brain injury (TBI). Here, data mining methods were combined with serial Glasgow Coma Scale (GCS) scores and clinical and laboratory parameters to predict 6-month functional outcome and mortality in patients with TBI. Data of consecutive adult patients presenting at a trauma center with moderate-to-severe head injury were retrospectively analyzed. Clinical parameters including serial GCS measurements at emergency department, 7th day, and 14th day and laboratory data were included for analysis (n = 115). We employed artificial neural network (ANN), naïve Bayes (NB), decision tree, and logistic regression to predict mortality and functional outcomes at 6 months after TBI. Favorable functional outcome was achieved by 34.8% of the patients, and overall 6-month mortality was 25.2%. For 6-month functional outcome prediction, ANN was the best model, with an area under the receiver operating characteristic curve (AUC) of 96.13%, sensitivity of 83.50%, and specificity of 89.73%. The best predictive model for mortality was NB with AUC of 91.14%, sensitivity of 81.17%, and specificity of 90.65%. Sensitivity analysis demonstrated GCS measurements on the 7th and 14th day and difference between emergency room and 14th day GCS score as the most influential attributes both in mortality and functional outcome prediction models. Analysis of serial GCS measurements using data mining methods provided additional predictive information in relation to 6-month mortality and functional outcome in patients with moderate-to-severe TBI.
Indexed as
Identifiers
25637541What 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.