Prevalence and temporal trends of cerebral palsy in children born from 2002 to 2017 in Ontario, Canada: Population‐based cohort study
Bibliographic record
Abstract
AIM: To examine the prevalence and temporal trends of cerebral palsy (CP) overall and by population characteristics. METHOD: We identified 2 110 177 live births born in the province of Ontario, Canada, between 2002 and 2017 using administrative health data and estimated CP prevalence in children aged 0 to 16 years overall and by specific population characteristics. We also examined temporal trends in CP rates - overall and by characteristics - in young children (0-4 years) by their year of birth between 2002 and 2013 (n=1 587 087 live births) to allow for an equal follow-up time (4 years and 364 days) for all children. RESULTS: Overall CP prevalence among children aged 0 to 16 years was 2.52 (95% confidence interval 2.45-2.59) per 1000 live births. CP rates in ages 0 to 4 years peaked at 2.86 in 2007 births, but steadily declined afterwards to 1.94 per 1000 live births in 2013. CP rates were higher in children born preterm, small for gestational age, males, multiples, children with congenital malformations, and in children of young (<20 years), old (≥40 years), primiparous, or grand multiparous (≥4) mothers; differences by these characteristics decreased over time. We observed socioeconomic disparities in CP rates that persisted over time. INTERPRETATION: Despite the decreasing trend of CP rates overall, CP rates varied by the child and maternal characteristics over time. WHAT THIS PAPER ADDS: Overall cerebral palsy (CP) prevalence was 2.5 per 1000 live births among children born from 2002 to 2017. CP prevalence peaked in children born in 2007 then steadily decreased between 2007 and 2013. Changes in CP rates varied over time by child and maternal characteristics. Socioeconomic inequalities in CP persisted and remained stable over the study period.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".