P.119 Emergency Department Use in Children with Cerebral Palsy: A Data Linkage Study
Bibliographic record
Abstract
Background: Improved understanding of factors predictive of emergency department (ED) visits in children with cerebral palsy (CP) can help optimize healthcare use. We sought to identify the pattern of ED consultations in these children. Methods: Data from the Registre de paralysie cérébrale du Québec and provincial administrative databases were linked. The CP cohort was comprised of children born between 1999 and 2002. Data pertaining to ED presentations between 1999 and 2012 were obtained. Relative risks were calculated to identify factors associated with increased ED visits. Peers without CP were selected from administrative databases and matched in a 20:1 ratio. Chi-square tests and Student’s T-tests were used to compare the two cohorts. Results: 301 children with CP and 6040 peer controls were selected. Ninety-two percent (92%) of the CP cohort had at least one ED visit, compared to 74% amongst controls. Children with CP had an increased risk of high ED use compared to peers (RR 1.40 95% CI 1.30-1.52). Factors predictive of high ED use were comorbid epilepsy, severe motor impairment and low socioeconomic status. Conclusions: Children with CP have a higher need for urgent health assessments than their peers, resulting in increased use of ED services. System factors and barriers should be investigated.
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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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".