Hospitalizations in School-Aged Children with Cerebral Palsy and Population-Based Controls
Bibliographic record
Abstract
OBJECTIVE: To compare hospitalizations among children with cerebral palsy (CP) and healthy controls and to identify factors associated with hospitalizations in children with CP. METHODS: This retrospective cohort study linked data from a provincial CP Registry and administrative health databases. The CP cohort was comprised of children born from 1999 to 2002. Age, sex, and region-matched controls were identified from administrative health databases. Mean differences, relative risk (RR), and 95% confidence intervals (CIs) were calculated. RESULTS: A total of 301 children with CP were linked to administrative health data and matched to 6040 controls. Mean hospitalizations per child during the study period were higher in children with CP compared to controls (raw mean difference (RMD) 5.0 95% CI 4.7 to 5.2) with longer length of stay (RMD 2.8 95% CI 1.8 to 3.8) and number of diagnoses per hospitalization (RMD 1.6 95% CI 1.4 to 1.8). Increased risk of hospitalization was observed in non-ambulant children with CP (RR 1.12 95% CI 1.01 to 1.22) compared to ambulant children and among those with spastic tri/quadriplegic CP compared to other CP subtypes (RR 1.15, 95% CI 1.05 to 1.27). Feeding difficulties (RR 1.20 95% CI 1.13 to 1.27), cortical visual (RR 1.22 95% CI 1.13 to 1.32), cognitive (RR 1.16 95% CI 1.04 to 1.30), and communication impairment (RR 1.26 95% CI 1.10 to 1.44) were associated with increased hospitalizations. CONCLUSIONS: Children with CP face more frequent, longer hospital stays than peers, especially those with a more severe CP profile. Coordinated interdisciplinary care is needed in school-aged children with CP and medical complexity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".