Changes to Memory Structures in Children Treated for Posterior Fossa Tumors
Bibliographic record
Abstract
Children treated for medulloblastoma (MB) exhibit long-term impairments in declarative memory, but the pathophysiology underlying this is unclear. Previous studies report declines in global white matter volume, but have failed to link this to declines in memory performance. We examined the effects of treatment on measures of global brain structure (i.e., total white and gray matter volume) and specific memory structures (i.e., hippocampus and uncinate fasciculus). We used volumetric MRI and diffusion tensor imaging in pediatric survivors of MB and one survivor of astrocytoma treated with cranial-spinal radiation (n = 20), and healthy controls (n = 13). Compared to controls, the survivor group exhibited reduced white matter volume, damage to the uncinate fasciculus, and a smaller right hippocampus. Critically, reduced hippocampal volume was not related to differences in brain volume, suggesting that the hippocampus may be especially vulnerable to treatment effects. A subset of the survivors (n = 10) also underwent memory testing using the Children's Memory Scale (CMS). Performance on the general index of the CMS was significantly correlated with measures of hippocampal volume and uncinate fasciculus. The examination of treatment effects on specific brain regions provides a better understanding of long-term cognitive outcome in children with brain tumors, particularly medulloblastoma.
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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 | 0.001 |
| 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 | 0.000 | 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".