Magnetic Resonance Imaging Criteria for Post-Concussion Syndrome: A Study of 127 Post-Concussion Syndrome Patients
Bibliographic record
Abstract
The purpose of this study was to assess the frequency of structural lesions on conventional magnetic resonance imaging (MRI) of the brain in a large prospective cohort of post-concussion syndrome (PCS) patients. Conventional 3T MRI was used to evaluate 127 prospectively enrolled PCS patients and 29 controls for non-specific white matter hyperintensities (WMH) and traumatic structural lesions, including encephalomalacia, atrophy, microhemorrhage, subarachnoid hemorrhage, and cortical siderosis. All PCS patients had a clinical diagnosis of one or more concussions based on the Concussion in Sport Group (CISG) consensus statements. Patients with recognized intracranial hemorrhage on prior head computed tomography (CT) and MRI were excluded. The differences between the PCS and control groups were analyzed. Four patients in the PCS group (3.1%) had positive findings, which included microhemorrhages in two patients and encephalomalacia in another two patients. None of these lesions was present in the control group, but there was no statistical difference between the two groups ( p = 0.5 for microhemorrhage and p = 0.5 for encephalomalacia). In the PCS group, 28 patients (22%) had WMH (15.7% had 1–10 lesions and 6.3% had >10 lesions), and these results did not differ from the age-matched control (20.6%, all with 1–10 lesions; p = 0.9) The location of the WMH showed no significant difference in the number of juxtacortical WMH between the PCS and control groups ( p = 0.5). Structural lesions were rare in PCS in this study, and the presence of such findings suggests a more severe form of traumatic brain injury. Our data support the role for MRI in the diagnosis of PCS by exclusion of atrophy, encephalomalacia, and all forms of intracranial hemorrhage. The presence of WMH irrespective of number is not an exclusion. This is the first description of the MRI criteria for PCS.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".