Relationship between ventricular size, white matter injury, and neurocognition in children with stable, treated hydrocephalus
Bibliographic record
Abstract
OBJECT: Larger-than-normal ventricles can persist in children following hydrocephalus treatment, even if they are asymptomatic and clinically well. This study aims to answer the following question: do large ventricles result in brain injuries that are detectable on diffusion tensor imaging (DTI) and/or in measurable neurocognitive deficits in children with stable, treated hydrocephalus that are not seen in children with small ventricles? METHODS: For this prospective study, we recruited 23 children (age range 8-18 years) with hydrocephalus due to aqueductal stenosis or tectal glioma who were asymptomatic following hydrocephalus treatment that had been performed at least 2 years earlier. All patients underwent detailed DTI and a full battery of neuropsychological tests. Correlation analysis was performed to assess the relationship between DTI parameters, neurocognitive tests, and ventricular size. The false-discovery rate method was used to adjust for multiple comparisons. RESULTS: The median age of these 23 children at the time of assessment was 15.0 years (interquartile range [IQR] 12.1-17.6 years), and the median age at the first hydrocephalus treatment was 5.8 years (IQR 2.2 months-12.8 years). At the time of assessment, 17 children had undergone endoscopic third ventriculostomy and 6 children had received a shunt. After adjusting for multiple comparisons, there were no significant correlations between any neurocognitive test and ventricular volume, any DTI parameter and ventricular volume, or any DTI parameter and neurocognitive test. CONCLUSIONS: Our data do not show an association between large ventricular size and additional white matter injury or worse neurocognitive deficits in asymptomatic children with stable, treated hydrocephalus caused by a discrete blockage of the cerebral aqueduct. Further investigations using larger patient samples are needed to validate these results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".