Neuroimaging in Pediatric Patients with Mild Traumatic Brain Injury: Relating the Current 2018 Centers for Disease Control Guideline and the Potential of Advanced Neuroimaging Modalities for Research and Clinical Biomarker Development
Bibliographic record
Abstract
The Center for Disease Control and Prevention (CDC)'s 2018 Guideline for current practices in pediatric mild traumatic brain injury (mTBI; also referred to as concussion herein) systematically identified the best up-to-date practices based on current evidence and, specifically, identified recommended practices regarding computed tomography (CT), magnetic resonance imaging (MRI), and skull radiograph imaging. In this article, we discuss types of neuroimaging not discussed in the guideline in terms of their safety for pediatric populations, their potential application, and the research investigating the future use of certain modalities to aid in the diagnosis and treatment of mTBI in children. The role of neuroimaging in pediatric mTBI cases should be considered for the potential contribution to children's neural and social development, in addition to the immediate clinical value (as in the case of acute structural findings). Selective use of specific neuroimaging modalities in research has already been shown to detect aspects of diffuse brain injury, disrupted cerebral blood flow, and correlate physiological factors with persistent symptoms, such as fatigue, cognitive decline, headache, and mood changes, following mTBI. However, these advanced neuroimaging modalities are currently limited to the research arena, and any future clinical application of advanced imaging modalities in pediatric mTBI will require robust evidence for each modality's ability to provide measurement of the subtle conditions of brain development, disease, damage, or degeneration, while accounting for variables at both non-injury and time-post-injury epochs. Continued collaboration and communication between researchers and healthcare providers is essential to investigate, develop, and validate the potential of advanced imaging modalities in pediatric mTBI diagnostics and management.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.037 | 0.146 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".