Age‐ and sex‐related effects in children with mild traumatic brain injury on diffusion magnetic resonance imaging properties: A comparison of voxelwise and tractography methods
Bibliographic record
Abstract
Although there are several techniques to analyze diffusion-weighted imaging, any technique must be sufficiently sensitive to detect clinical abnormalities. This is especially critical in disorders like mild traumatic brain injury (mTBI), where pathology is likely to be subtle. mTBI represents a major public health concern, especially for youth under 15 years of age. However, the developmental period from birth to 18 years is also a time of tremendous brain changes. Therefore, it is important to establish the degree of age- and sex-related differences. Participants were children aged 8-15 years with mTBI or mild orthopedic injuries. Imaging was obtained within 10 days of injury. We performed tract-based spatial statistics (TBSS), deterministic tractography using Automated Fiber Quantification (AFQ), and probabilistic tractography using TRACULA (TRActs Constrained by UnderLying Anatomy) to evaluate whether any method provided improved sensitivity at identifying group, developmental, and/or sex-related differences. Although there were no group differences from any of the three analyses, many of the tracts, but not all, revealed increases of fractional anisotropy and decreases of axial, radial, and mean diffusivity with age. TBSS analyses resulted in age-related changes across all white matter tracts. AFQ and TRACULA revealed age-related changes within the corpus callosum, cingulum cingulate, corticospinal tract, inferior and superior longitudinal fasciculus, and uncinate fasciculus. The results are in many ways consistent across all three methods. However, results from the tractography methods provided improved sensitivity and better tract-specific results for identifying developmental and sex-related differences within the brain.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".