Epilepsy in children with cerebral palsy: a data linkage study
Bibliographic record
Abstract
AIM: To compare the prevalence of epilepsy in children with cerebral palsy (CP) to peer controls and their differences in healthcare utilization. METHOD: The Quebec CP registry was linked to the provincial administrative health database. Two CP cohorts were identified from the registry (n=302, 168 males, 1y 2mo-14y) and administrative data (n=370, 221 males, 2y 2mo-14y). A control cohort (n=6040, 3340 males, 10-14y) was matched by age, sex, and region to the CP registry cohort. Administrative data algorithms were used to define epilepsy cases. Data on hospitalizations and emergency department presentations were obtained. RESULTS: Using the most sensitive epilepsy definition, prevalence was 42.05% in the CP registry, 43.24% in the CP administrative data, and 1.39% in controls. Prevalence rose with increasing Gross Motor Function Classification System level. Children with CP and epilepsy had increased number and length of hospitalizations and emergency department presentations compared to children with CP or epilepsy alone. Epilepsy accounted for approximately 5% of emergency department presentations and 10% of hospitalizations in children with epilepsy, with and without CP. INTERPRETATION: Children with CP have an increased risk of epilepsy compared to their peers. Children with CP and coexisting epilepsy represent a unique subset with complex developmental disability and increased healthcare service utilization.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".