Cerebellar malformations alter regional cerebral development
Bibliographic record
Abstract
AIM: The aim of this study was to compare total and regional cerebral volumes in children with isolated cerebellar malformations (CBMs) with those in typically developing children, and to examine the extent to which cerebellar volumetric reductions are associated with total and regional cerebral volumes. METHOD: This is a case-control study of children diagnosed with isolated CBMs. Each child was matched on age and sex to two typically developing children. Using advanced three-dimensional volumetric magnetic resonance imaging, the cerebrum was segmented into tissue classes and partitioned into eight regions. Analysis of variance was used to compare cerebral volumes between children with CBMs and control children, and linear regressions to examine the impact of cerebellar volume reduction on cerebral volumes. RESULTS: Magnetic resonance imaging was performed at a mean age of 27 months in 20 children (10 males, 10 females) with CBMs and 40 typically developing children. Children with CBMs showed significantly smaller deep grey matter nuclei (p < 0.001), subgenual white matter (p = 0.03), midtemporal white matter (p = 0.02), and inferior occipital grey matter (p = 0.03) volumes than typically developing children. Greater cerebellar volumetric reduction in children with CBMs was associated with decreased total cerebral volume and deep grey matter nuclei (p = 0.02), subgenual white/grey matter (p = 0.001), midtemporal white (p = 0.02) and grey matter (p = 0.01), and parieto-occipital grey matter (p = 0.004). INTERPRETATION: CBMs are associated with impaired regional cerebral growth, suggesting deactivation of principal cerebello-cerebral pathways.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.002 | 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".