Trial reportBMC emergency medicine2016
Early prediction of outcome after severe traumatic brain injury: a simple and practical model.
Trial report in BMC emergency medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
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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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed.
- Beyond Acute Kidney Injury : Direct Pathways Implicate Inflammatory Mechanisms in Serum NGAL's Mortality Effect in Acute Brain Injury.Translational stroke research · 2026Article
- Evaluation of Traumatic Brain Injury Severity Using the Abbreviated Injury Scale and the Injury Severity Score: A Retrospective Study in Two Eastern European Centers.Journal of clinical medicine · 2025Article
- Predicting outcomes after moderate and severe traumatic brain injury using artificial intelligence: a systematic review.NPJ digital medicine · 2025Article
- Prediction of prognosis in patients with cerebral contusions based on machine learning.Scientific reports · 2024Article
- Development and validation of a nomogram for predicting mortality in patients with acute severe traumatic brain injury: A retrospective analysis.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2024Article
- Article
- "NeuroVanguard": a contemporary strategy in neuromonitoring for severe adult brain injury patients.Critical care (London, England) · 2024Review
- The Use of Artificial Intelligence to Predict the Prognosis of Patients Undergoing Central Nervous System Rehabilitation: A Narrative Review.Healthcare (Basel, Switzerland) · 2023Review
- An independently validated nomogram for individualised estimation of short-term mortality risk among patients with severe traumatic brain injury: a modelling analysis of the CENTER-TBI China Registry Study.EClinicalMedicine · 2023Article
- Machine learning algorithms for predicting outcomes of traumatic brain injury: A systematic review and meta-analysis.Surgical neurology international · 2023Review
- Integrating unsupervised and supervised learning techniques to predict traumatic brain injury: A population-based study.Intelligence-based medicine · 2023Article
- Outcomes of traumatic brain injury patients in an adult intensive care unit of a South African regional hospital, without on-site neurosurgical service: A retrospective quantitative study on the neurological improvement at discharge.The Southern African journal of critical care : the official journal of the Critical Care Society · 2023Article
- Long-Term Outcomes in Severe Traumatic Brain Injury and Associated Factors: A Prospective Cohort Study.Journal of clinical medicine · 2022Article
- An interpretable neural network for outcome prediction in traumatic brain injury.BMC medical informatics and decision making · 2022Article
- All body region injuries are not equal: Differences in pediatric discharge functional status based on Abbreviated Injury Scale (AIS) body regions and severity scores.Journal of pediatric surgery · 2022Article
- A Framework to Advance Biomarker Development in the Diagnosis, Outcome Prediction, and Treatment of Traumatic Brain Injury.Journal of neurotrauma · 2022Review
- The Outcome Predictors of the Patients with Traumatic Brain Injury; A Cross-Sectional Study.Bulletin of emergency and trauma · 2022Article
- A hierarchical expert-guided machine learning framework for clinical decision support systems: an application to traumatic brain injury prognostication.NPJ digital medicine · 2021Article
- Modelling outcomes after paediatric brain injury with admission laboratory values: a machine-learning approach.Pediatric research · 2019Article
- The aggressiveness of neurotrauma practitioners and the influence of the IMPACT prognostic calculator.PloS one · 2017Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
backgroundTraumatic brain injury (TBI) is a heterogeneous syndrome with a broad range of outcome. We developed a simple model for long-term outcome prognostication after severe TBI.
methodsSecondary data analysis of a large multicenter randomized trial. Patients were grouped according to 6-month extended Glasgow outcome scale (eGOS): poor-outcome (eGOS ≤ 4; severe disability or death) and acceptable outcome (eGOS > 4; no or moderate disability). A prediction decision tree was built using binary recursive partitioning to predict poor or acceptable 6-month outcome. Comparison to two previously published and validated models was made.
resultsThe decision tree included the predictors of head Abbreviated Injury Scale (AIS) severity, the Marshall computed tomography score, and pupillary reactivity. All patients with a head AIS severity of 5 were predicted to have a poor outcome. In patients with head AIS severity < 5, the model predicted an acceptable outcome for (1) those with Marshall score of 1, and (2) those with Marshall score above 1 but with reactive pupils at admission. The decision tree had a sensitivity of 72.3 % (95 % CI: 66.4-77.6 %) and specificity of 62.5 % (95 % CI: 54.9-69.6 %). The proportion correctly classified for the comparison models was similar to our model. Our model was more apt at correctly classifying those with poor outcome but more likely to misclassify those with acceptable outcome than the comparison models.
conclusionPredicting long-term outcome early after TBI remains challenging and inexact. This model could be useful for research and quality improvement studies to provide an early assessment of injury severity, but is not sufficiently accurate to guide decision-making in the clinical setting.
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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.