An update on the prevalence of cerebral palsy: a systematic review and meta‐analysis
Bibliographic record
Abstract
AIMS: The aim of this study was to provide a comprehensive update on (1) the overall prevalence of cerebral palsy (CP); (2) the prevalence of CP in relation to birthweight; and (3) the prevalence of CP in relation to gestational age. METHOD: A systematic review and meta-analysis was conducted and reported, based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) statement. Population-based studies on the prevalence of CP in children born in 1985 or after were selected. Statistical analysis was carried out using computer package R, version 2.14. RESULTS: A total of 49 studies were selected for this review. The pooled overall prevalence of CP was 2.11 per 1000 live births (95% confidence interval [CI] 1.98-2.25). The prevalence of CP stratified by gestational age group showed the highest pooled prevalence to be in children weighing 1000 to 1499g at birth (59.18 per 1000 live births; 95% CI 53.06-66.01), although there was no significant difference on pairwise meta-regression with children weighing less than 1000g. The prevalence of CP expressed by gestational age was highest in children born before 28 weeks' gestation (111.80 per 1000 live births; 95% CI 69.53-179.78; p<0.0327). INTERPRETATION: The overall prevalence of CP has remained constant in recent years despite increased survival of at-risk preterm infants.
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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 | 0.028 | 0.069 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.020 |
| Bibliometrics | 0.017 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".