Microstructure of the Corpus Callosum Long after Pediatric Concussion
Bibliographic record
Abstract
OBJECTIVE: The long-term effects of pediatric concussion on white matter microstructure are poorly understood. This study investigated long-term changes in white matter diffusion properties of the corpus callosum in youth several years after concussion. METHODS: Participants were 8-19 years old with a history of concussion (n = 36) or orthopedic injury (OI) (n = 21). Mean time since injury for the sample was 2.6 years (SD = 1.6). Participants underwent diffusion magnetic resonance imaging, completed cognitive testing, and rated their post-concussion symptoms. Measures of diffusivity (fractional anisotropy, mean, axial, and radial diffusivity) were extracted from white matter tracts in the genu, body, and splenium regions of the corpus callosum. The genu and splenium tracts were further subdivided into 21 equally spaced regions along the tract and diffusion values were extracted from each of these smaller regions. RESULTS: White matter tracts in the genu, body, and splenium did not differ in diffusivity properties between youth with a history of concussion and those with a history of OI. No significant group differences were found in subdivisions of the genu and splenium after correcting for multiple comparisons. Diffusion metrics did not significantly correlate with symptom reports or cognitive performance. CONCLUSIONS: These findings suggest that at approximately 2.5 years post-injury, youth with prior concussion do not have differences in their corpus callosum microstructure compared to youth with OI. Although these results are promising from the perspective of long-term recovery, further research utilizing longitudinal study designs is needed to confirm the long-term effects of pediatric concussion on white matter microstructure.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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".