Diffusion tensor imaging of white matter after cranial radiation in children for medulloblastoma: Correlation with IQ
Bibliographic record
Abstract
Treatment of children with cranial-spinal radiation (CSR) for brain tumors is associated with adverse intellectual outcome and white matter damage. However, the correlation between IQ and measures of white matter integrity has received little attention. We examined apparent diffusion coefficient (ADC), fractional anisotropy (FA), and intelligence in pediatric patients treated with CSR for medulloblastoma relative to control subjects. ADC and FA measures were obtained for eight patients and eight control children and evaluated in multiple regions of interest in the cerebral hemispheres. Mean ADC and mean FA for each region were calculated, group differences were evaluated, and the relationship between these measures and intelligence were examined. In our study group, decreased IQ was associated with increased ADC and decreased FA (P < 0.01). Mean IQ for the CSR group was lower than that for the control group, but the difference was not significant when controlling for overall mean FA or ADC (P > 0.10). Overall mean FA was lower and ADC was higher in the CSR group relative to controls (P < 0.01). Specifically, FA was lower in the genu of the corpus callosum, the anterior and posterior limbs of the internal capsule, inferior frontal white matter, and high frontal white matter, and ADC was higher in all regions in patients relative to controls (P < 0.01). Compromised white matter integrity was observed for multiple regions within the cerebral hemispheres following CSR. A novel finding was that microscopic damage in normal-appearing white matter, as indexed by higher ADC and lower FA, was related to poor intellectual outcome relative to age-matched controls.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".