Supplementary Material for: Prevalence of Cerebral Palsy in Quebec: Alternative Approaches
Bibliographic record
Abstract
Aim: To provide an estimate of the period prevalence of cerebral palsy (CP) in the province of Quebec. Methods: Children with CP were identified from three consecutive birth cohorts (1999–2001) from the Quebec CP Registry, covering 6 of the 17 administrative health regions of the province. Two inferential approaches were applied for period prevalence estimation, frequentist and bayesian. Results: 228 children were identified with CP. Using a frequentist approach, the overall prevalence of CP was 1.84 per 1,000 children aged 9–11 years living in those areas in 2010 (95% CI 1.60–2.08). Using a bayesian approach taking into account the uncertainty about the registry’s sensitivity in capturing all cases, the overall prevalence is higher at 2.30 per 1,000 children with a 95% CI (1.99–2.65). Conclusion: Using a bayesian approach to adjust for the registry’s known high specificity and lower sensitivity, the prevalence estimate is in concordance with worldwide estimates and estimates using administrative databases in western Canadian provinces. Future studies are needed to validate the diagnosis of CP within administrative databases and to evaluate possible regional trends across Canada in both prevalence and health service utilization, which may highlight disparities in healthcare delivery.
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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 | 0.002 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.645 | 0.078 |
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".