Observational studyPloS one2023
Applicability of machine learning technique in the screening of patients with mild traumatic brain injury.
Observational study in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 10 papers, 1 of them a synthesis that pooled it.
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
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 14 citations in OpenAlex.
- Risk factors for traumatic intracranial hemorrhage in mild traumatic brain injury patients at the emergency department: a systematic review and meta-analysis.Scandinavian journal of trauma, resuscitation and emergency medicine · 2024Pooled it
- Review
- Prediction of Clinically Significant Improvements During the Interdisciplinary Intensive Outpatient Program for Traumatic Brain Injury Using Machine Learning.Annals of biomedical engineering · 2025Article
- Global emergency medicine: A scoping review of the literature from 2023.Academic emergency medicine : official journal of the Society for Academic Emergency Medicine · 2025Article
- An interpretable machine learning model based on optimal feature selection for identifying CT abnormalities in patients with mild traumatic brain injury.EClinicalMedicine · 2025Article
- Machine Learning Models for Predicting Abnormal Brain CT Scan Findings in Mild Traumatic Brain Injury Patients.Archives of academic emergency medicine · 2025Article
- Potential Applications and Ethical Considerations for Artificial Intelligence in Traumatic Brain Injury Management.Biomedicines · 2024Review
- Consideration of Brain CT Imaging Standard for Mild Head Injuries.Neurologia medico-chirurgica · 2024Article
- Applicability of machine learning algorithm to predict the therapeutic intervention success in Brazilian smokers.PloS one · 2024Article
- Correction: Applicability of machine learning technique in the screening of patients with mild traumatic brain injury.PloS one · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
12 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Even though the demand of head computed tomography (CT) in patients with mild traumatic brain injury (TBI) has progressively increased worldwide, only a small number of individuals have intracranial lesions that require neurosurgical intervention. As such, this study aims to evaluate the applicability of a machine learning (ML) technique in the screening of patients with mild TBI in the Regional University Hospital of Maringá, Paraná state, Brazil. This is an observational, descriptive, cross-sectional, and retrospective study using ML technique to develop a protocol that predicts which patients with an initial diagnosis of mild TBI should be recommended for a head CT. Among the tested models, he linear extreme gradient boosting was the best algorithm, with the highest sensitivity (0.70 ± 0.06). Our predictive model can assist in the screening of mild TBI patients, assisting health professionals to manage the resource utilization, and improve the quality and safety of patient care.
Indexed as
Identifiers
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.