CENTRAL NERVOUS SYSTEM MALFORMATIONS IN CHILDHOOD: DIAGNOSIS AND INCIDENCE COMPARED TO TUBEROUS SCLEROSIS
Bibliographic record
Abstract
Objectives: To estimate the frequency and neurodevelopmental outcome of central nervous system (CNS) malformations in the pediatric age group. Methods: A retrospective chart review of children diagnosed with CNS malformations or tuberous sclerosis (TS) between 1995–2005 was undertaken. Cases of acquired CNS lesions were excluded. Change in diagnosis after MRI became available was noted. Demographic data and clinical characteristics including estimates of IQ, presence and type of seizure, behavioural disturbances, speech delay, motor deficit, hearing loss, and cortical vision defects were noted. Results: Queen's University affiliated hospitals have a referral base of approximately 300 000 population. Between 1995–2005, 24 children with CNS malformations were recognized compared with 2 children with TS. Partial or complete agenesis of the corpus callosum was the most common diagnosis (42%), which was diagnosed by CT scan in 90%. Seven (29%) have a diagnosis of Dandy-Walker syndrome or mega cisterna magna. Four (16%) were diagnosed with heterotropia (50% by CT; 50% by MRI). Two children (8%) have a possible diagnosis of heterotropia based on CT scan and are awaiting MRI for confirmation. Three children (12.5%) were diagnosed with polygyria and 2 children (8%) with schizencephaly after MRI. Neurodevelopmental outcome ranges from normal to severe impairment. Conclusion: CNS malformations are increasingly recognized with availability of MRI in the pediatric population. These abnormalities are missed on early ultrasound screening during pregnancy. The neurodevelopmental outcomes of these children are dependant on the extent of CNS malformation. MRI is the imaging modality of choice in diagnosing these lesions. In this series, CNS malformations were 12 times more common than TS, a well characterized genetic brain abnormality which is much more familiar to pediatric neurologists.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".