B.5 Hospitalization in school aged children with cerebral palsy and population-based Controls: A Data Linkage Study
Bibliographic record
Abstract
Background: Predictors of hospitalization and reasons for admissions can inform healthcare planning and prevention. We sought to characterize the hospitalization pattern and risk factors for admission of children with cerebral palsy (CP). Methods: Data from the Registre de paralysie cérébrale du Québec and provincial administrative databases were linked. The CP cohort contained children born between 1999 and 2002. Data related to admissions were captured in 2012. Relative risks (RR) were calculated to identify factors increasing hospitalization risk. Peers without CP were matched from administrative databases in a 20:1 ratio. Chi-square tests and Student’s T-tests were used to compare cohorts. Results: 301 children with CP and 6040 peer controls were selected. Hospitalizations were increased in children with CP (raw mean difference (RMD) 5.0 95% CI 4.7 to 5.2), with significantly longer lengths 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 any hospitalization was observed in children with a more complex profile. Conclusions: Children with a more severe profile of CP and greater health care complexity face more frequent and longer hospital stays. 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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".