Cerebral palsy characteristics in term‐born children with and without detectable perinatal risk factors: A cross‐sectional study
Bibliographic record
Abstract
AIM: To compare, in term-born children with cerebral palsy (CP), the characteristics of those who exhibit detectable risk factors for CP at birth with those who do not. METHOD: This was a cross-sectional study of term-born children using the Canadian Cerebral Palsy Registry comparing those with and without perinatal risk factors and/or neonatal symptoms for pregnancy, birth and neonatal characteristics, magnetic resonance imaging (MRI) findings, CP subtype, and impairment severity. Risk factors were quantified with a CP risk calculator. Multivariable and multinomial regressions were expressed as odds ratios (OR) and relative risk ratios. RESULTS: Of 1333 term-born children, 781 (58.6%) had complete variables for the CP risk calculator, of whom 195 (25%) had 'undetectable' newborn infant CP risk, and they did not have greater postneonatal brain injury. Focal injury on MRI was more common (OR 2.0, 95% confidence interval [CI] 1.3-3.1) than in the 'detectable' group. The 'undetectable' group had more unilateral CP (OR 1.8, 95% CI 1.3-2.6), less severe motor impairment (OR 0.76, 95% CI 0.67-0.86), and were more verbal (OR 2.3, 95% CI 1.5-3.6). INTERPRETATION: In the Canadian CP Registry, one-quarter of term-born children lacked neonatal encephalopathy, seizures, or perinatal risk factors. They were more likely to have unilateral CP, focal MRI findings, and communicate with words than children with risk factors or neonatal symptoms.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".