The first week after concussion: Blood flow, brain function and white matter microstructure
Bibliographic record
Abstract
Concussion is a major health concern, associated with short-term deficits in physical function, emotion and cognition, along with negative long-term health outcomes. However, we remain in the early stages of characterizing MRI markers of concussion, particularly during the first week post-injury when symptoms are most severe. In this study, 52 varsity athletes were scanned using Magnetic Resonance Imaging (MRI), including 26 athletes with acute concussion (scanned 1-7 days post-injury) and 26 matched control athletes. A comprehensive set of functional and structural MRI measures were analyzed, including cerebral blood flow (CBF) and global functional connectivity (Gconn) of grey matter, along with fractional anisotropy (FA) and mean diffusivity (MD) of white matter. An analysis comparing acutely concussed athletes and controls showed limited evidence for reliable mean effects of acute concussion, with only MD showing spatially extensive differences between groups. We subsequently demonstrated that the number of days post-injury explained a significant proportion of inter-subject variability in MRI markers of acutely concussed athletes. Athletes scanned at early acute injury (1-3 days) had elevated CBF and Gconn and reduced FA, but those scanned at late acute injury (5-7 days) had the opposite response. In contrast, MD showed a more complex, spatially-dependent relationship with days post-injury. These novel findings highlight the variability of MRI markers during the acute phase of concussion and the critical importance of considering the acute injury time interval, which has significant implications for studies relating acute MRI data to concussion outcomes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".