41: Variation in Incidence of Cerebral Palsy (CP) in Preterm Infants in the Canadian Neonatal Follow-Up Network (CNFUN): 2009–2011
Bibliographic record
Abstract
The Canadian Neonatal Follow-Up Network (CNFUN) has followed the developmental outcomes of all survivors of preterm birth <29 weeks gestation in 26 centres across Canada. The variation in the incidence of CP in Canadian NICUs is not known. To determine the incidence, severity and type of cerebral palsy in preterm infants admitted to CNFUN NICUs in Canada. Preterm infants born <29 weeks of gestational age (GA) in CNFUN between April 1, 2009 and July 1, 2011 were evaluated between 18 and 24 months corrected age (CA) using standardized criteria. Demographic and neurodevelopmental outcomes, specifically incidence, severity and type of CP were compared according to GA and birth weight (BW) categories by using Pearson χ2 or Fisher exact test for categorical variables and ANOVA F-test for continuous variables. Of 2528 infants, 419 were not followed up and CP status was missing in 45. The number of infants evaluated as confirmed CP, suspect CP and no CP were 140 (6.8%), 72 (3.5%) and 1852 (89.7%) respectively. In CP, suspect CP and no CP groups, the mean BW was 908 g (223), 929 g (205) and 943 g (224) (P=0.18) and median GA was 26 weeks (25 to 27 weeks), 27 weeks (25 to 27 weeks) and 27 weeks (25 to 28 weeks) (P<0.01) respectively. The incidence, severity and type of CP by GA and BW are shown in Table 1 and Figure 1 respectively. Variation in incidence of cerebral palsy was observed in preterm infants born in Canada's NICUs. This study provides population-based information about incidence of cerebral palsy in preterm infants admitted to Level 3 NICUs in Canada over a two-year period. The incidence of CP is greatest in the extremely preterm infants born <26 weeks of GA. The incidence of CP varies among CNFUN centers and in various centres in Canada will be explored further.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".