Can quantitative susceptibility mapping help diagnose and predict recovery of concussion in children? An A-CAP study
Bibliographic record
Abstract
BACKGROUND: Quantitative susceptibility mapping (QSM) is an MRI technique that is a potential biomarker for concussion. We performed QSM in children following concussion or orthopaedic injury (OI), to assess QSM performance as a diagnostic and prognostic biomarker. METHODS: Children aged 8-17 years with either concussion (N=255) or OI (N=116) were recruited from four Canadian paediatric emergency departments and underwent QSM postacutely (2-33 days postinjury) using 3 Tesla MRI. QSM Z-scores within nine regions of interest (ROI) were compared between groups. QSM Z-scores were also compared with the 5P score, the current clinical benchmark for predicting persistent postconcussion symptoms (PPCS), at 4 weeks postinjury, with PPCS defined using reliable change methods based on both participant and parent reports. RESULTS: Concussion and OI groups did not differ significantly in QSM Z-scores for any ROI. Higher QSM Z-scores within frontal white matter (WM) independently predicted PPCS based on parent ratings of cognitive symptoms (p=0.001). The combination of frontal WM QSM Z-score and 5P score was better at predicting PPCS than 5P score alone (p=0.004). The area under the curve was 0.72 (95% CI 0.63 to 0.81) for frontal WM susceptibility, 0.69 (95% CI 0.59 to 0.79) for the 5P score and 0.74 (95% CI 0.65 to 0.83) for both. CONCLUSION: The findings suggest that QSM is a potential MRI biomarker that can help predict PPCS in children with concussion, over and above the current clinical benchmark, and thereby aid in clinical management. They also suggest a frontal lobe substrate for PPCS, highlighting the potential for QSM to clarify the neurophysiology of paediatric concussion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".