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 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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".