Corpus callosum abnormalities: neuroradiological and clinical correlations
Bibliographic record
Abstract
AIM: To study neuroradiological features in pediatric patients with corpus callosum abnormalities, using new functional subtyping for the corpus callosum, and to correlate the features with the clinical presentation. METHOD: We performed a retrospective review of 125 patients with radiologically identified abnormalities of the corpus callosum seen between 1999 and 2012. The study reviewed clinical features, genetic etiology, and chromosomal microarray (CMA) results. We used a new functional classification for callosal abnormalities based on embryological and anatomical correlations with four classes: complete agenesis, anterior agenesis (rostrum, genu, body), posterior agenesis (isthmus, splenium), and complete hypoplasia (thinning). We also studied the presence of extracallosal abnormalities. RESULTS: The new functional callosal subtyping did not reveal significant differences between the various subtypes in association with neurological outcome; however, the presence of cardiac disease was found more frequently in the group with complete agenesis. Thirty-seven per cent (46/125) had identifiable causes: of these, 48% (22/46) had a monogenic disorder, 30% (14/46) had a pathogenic chromosomal copy-number variant detected by CMA or karyotype, and 22% (10/46) had a recognizable clinical syndrome for which no confirmatory genetic test was available (namely Aicardi syndrome/septo-optic dysplasia and Goldenhar syndrome). The diagnostic yield for a significant CMA change was 19%. The presence of Probst bundles was found to be associated with a better neurodevelopmental outcome. INTERPRETATION: The functional classification system alone 'without clinical data' cannot predict the functional outcome. The presence of extracallosal brain abnormalities and an underlying genetic diagnosis predicted a worse neurodevelopmental outcome. This study highlights the importance of CMA testing and cardiac evaluation as part of a routine screen.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".