Quantitative diffusion tensor imaging and intellectual outcomes in spina bifida
Bibliographic record
Abstract
OBJECT: Patients with spina bifida (SB) have variable intellectual outcomes. The authors used diffusion tensor (DT) imaging to quantify whole-brain volumes of gray matter, white matter, and cerebrospinal fluid (CSF), and perform regional quantitative microstructural assessments of gray matter nuclei and white matter tracts in relation to intellectual outcomes in patients with SB. METHODS: Twenty-nine children with myelomeningoceles and 20 age- and sex-matched children with normal neural tube development underwent MR imaging with DT image acquisition and assessments of intelligence. The DT imaging-derived metrics were the fractional anisotropy (FA), axial (parallel), and transverse (perpendicular) diffusivities. These metrics were also used to segment the brain into white matter, gray matter, and CSF. A region-of-interest analysis was conducted of the white and gray matter structures implicated in hydrocephalus. RESULTS: The amount of whole-brain gray matter was decreased in patients with SB, with a corresponding increase in CSF (p < 0.0001). Regional transverse diffusivity in the caudate nucleus was decreased (p < 0.0001), and the corresponding FA was increased (p < 0.0001), suggesting reduced dendritic branching and connectivity. Fractional anisotropy in the posterior limb of the internal capsule increased in the myelomeningocele group (p = 0.02), suggesting elimination of some divergent fascicles; in contrast, the FA in several white matter structures (such as the corpus callosum genu [p < 0.001] and arcuate fasciculus) was reduced, suggesting disruption of myelination. Diffusion tensor imaging-metrics involving gray matter volume and the caudate nucleus, but not other structures, predicted variations in IQ (r = 0.37-0.50; p < 0.05). CONCLUSIONS: Diffusion tensor imaging-derived metrics provide noninvasive neuronal surrogate markers of the pathogenesis of SB and predict variations in general intellectual outcomes in children with this condition.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".