Congenital non‐central nervous system malformations in cerebral palsy: a distinct subset?
Bibliographic record
Abstract
AIM: The aim of this article was to identify and contrast the subset of children with cerebral palsy (CP) and non-central nervous system (CNS) congenital malformations with children with CP but no coexisting non-CNS congenital malformations. METHOD: A population-based regional comprehensive CP registry was used to identify children with CP who had non-CNS congenital malformations (n = 34; 19 males, 15 females; 22 classified as Gross Motor Function Classification System [GMFCS] levels I-III, 12 as GMFCS level IV or V). Their clinical features were then compared with other children with CP without non-CNS congenital malformations (n = 207; 115 males, 92 females; 138 classified as GMFCS levels I-III, 69 as GMFCS level IV or V). RESULTS: Children with CP and non-CNS congenital malformations did not differ from those without in terms of neurological subtype distribution or functional severity, as measured by the GMFCS. Also, there was no association with previous maternal infections (i.e. toxoplasmosis, rubella, cytomegalovirus, herpes simplex virus 2 [TORCH]), maternal fever, use of illicit substances, asphyxia, neonatal encephalopathy, intraventricular haemorrhage, or septicaemia. The incidence of comorbidities such as convulsions, communication difficulties, gavage feeding, cortical blindness, and auditory impairment was not higher in this subgroup. INTERPRETATION: The incidence of congenital non-CNS malformations among children with CP is appreciable. Children with these non-CNS malformations do not appear to differ from other children with CP regarding neurological subtype, functional severity, and comorbidities, or maternal or obstetrical factors. Thus, the specific presence of a non-CNS congenital malformation does not appear to assist the practitioner in the management or understanding of a child's CP.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".