Can Quantitative Susceptibility Mapping Help Diagnose and Predict Recovery of Concussion in Children?
Bibliographic record
Abstract
Background: Following mild traumatic brain injury (mTBI), also termed concussion, 15-30% of children experience symptoms lasting four weeks or more that reduce their quality of life. Conventional clinical neuroimaging is insensitive to mTBI; however, given the possibility of a neuroinflammatory response following concussion, there is potential for an MRI sequence called quantitative susceptibility mapping (QSM) as a biomarker for injury. In the largest cohort to date, we compared QSM in pediatric concussion patients versus a comparison group of children with orthopedic injuries (OI) and assessed QSM’s performance relative to the current clinical benchmark (5P risk score) for predicting persistent post-concussion symptoms (PPCS). Methods: Children (N=967) aged 8-16.99 years with mTBI or OI were recruited from 5 Canadian pediatric emergency departments. Participants completed QSM at a post-acute assessment (2-33 days post-injury). QSM z-score metrics of susceptibility within 9 regions of interest (ROI) were derived from 371 children (mTBI=255, OI=116). PPCS at 1-month post-injury was defined using reliable change methods. Results: Multivariable linear regression analyses did not reveal a statistically significant difference in susceptibility between mTBI and OI children in any ROI. Multivariable logistic regression analyses revealed increased frontal WM susceptibility was significantly associated with predicting parent-rated reliable change in persistent concussion cognitive symptoms (p=0.001). Frontal WM susceptibility also was nominally significant in a model with all nine regions included (p=0.013). The model with frontal WM and the 5P risk score performed better at predicting parent-rated reliable change in cognitive symptoms than the model with the 5P risk score alone (p=0.002). The area under the curve (AUC) was 0.71(95%CI: 0.62-0.80) for frontal WM susceptibility, 0.67(95%CI: 0.56-0.78) for the 5P risk score, and 0.73(95%CI: 0.64-0.82) for both. Conclusion: We believe this may be the first study to demonstrate a potential imaging biomarker that predicts persistent symptoms using reliable change in children with concussion compared to the current clinical benchmark. Our findings not only suggest a potential neuropathological substrate associated with persistent symptoms, but also highlights the potential for using neuroimaging to assist in the clinical management of concussion in children.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 teacher head, 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".