P.105 Corpus callosum changes in children affected by infantile hydrocephalus
Bibliographic record
Abstract
Background: Infantile hydrocephalus is characterized by an atypical accumulation of cerebrospinal fluid in the brain, diagnosed and treated before the age of 2 years. Hydrocephalus development is linked to thinning of the corpus callosum (CC), mainly due to the expansion of lateral ventricles, causing upward elevation and compression of periventricular and subcortical white matter. Methods: This study investigates structural alterations in the CC in children diagnosed with infantile hydrocephalus. We examined both macrostructural and microstructural facets of the CC, providing insights into the nature and extent of alterations associated with this condition. 18 patients with infantile hydrocephalus (mean age = 9 years), and 18 age and sex matched typically-developing healthy children, participated in the study. Structural magnetic resonance imaging and diffusion tensor imaging were utilized to assess CC volume and microstructure, respectively. Results: Our findings reveal reductions in CC volume, particularly in posterior area, and distinct microstructural disparities, notably pronounced in these same segments. Conclusions: Investigating these structural alterations provides an understanding into the mechanisms underlying the effects of infantile hydrocephalus on CC integrity, given its role as a neural bridge. This knowledge offers a more nuanced perspective on neurological disorders and underscores the significance of investigating the CC’s health in such contexts.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.003 | 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".