86 Sociodemographic and family characteristics of children with and without neurodevelopmental impairment in a Canadian cohort of extreme preterm children
Bibliographic record
Abstract
Abstract Background Despite improved survival of extreme preterm infants (<29 weeks’ gestation), neurodevelopmental impairment (NDI) remains high. Certain sociodemographic characteristics (SDC) and limited family resources are known to adversely affect children health and development, particularly those born preterm. There is a dearth of recent data from large cohorts examining the impact of social determinants of health on the neurodevelopment of preterm children. Objectives (1) to describe SDC and family composition in a population-based cohort of extreme preterm children using the Canadian Neonatal Follow Up Network (CNFUN) database; (2) to develop a logistic regression model examining the association of significant NDI (sNDI any of: CP ≥ GMFCS stage 3, Bayley III < 70 in any domain, deafness requiring aids, or bilateral blindness) with SDC using a unique family descriptor. Design/Methods A retrospective review of a national cohort of extreme preterm infants (230 -286 weeks) born between April 2009 and December 2018. We included children who had neurodevelopmental assessment at 18-24 month corrected age at a CNFUN participating Centre. We compared SDC of infants and caregivers and family descriptors (moderating variables) between children with no NDI and those with any NDI and sNDI. Multivariate logistic regression models were developed in two steps to evaluate the effect of SDC (Model 1) and family composition (Model 2) on the primary outcome of sNDI. GEE was used to account for clustering by multiples and within site. Results Out of 10833 eligible infants, 6219 (57%) were included. Of those, 3412 (55%) had no NDI and 2807 (45%) had any NDI. sNDI accounted for 17% of the cohort and 37% of those with NDI. Comparison of the SDC and family descriptors between the three groups are shown in Table 1. On multivariate analysis using SDC, infant’s gestational age and male sex, and primary caregiver’s ethnicity and level of education were independently associated with sNDI. Immigration and employment status were associated with sNDI when family descriptors were added to the first model (Table 2). Conclusion In this national cohort, half of extreme preterm children developed NDI and 1 in 7 had sNDI. There were differences in sociodemographic and family characteristics between those with and without sNDI. The degree of prematurity and caregiver’s education, employment and immigration status were independently associated with sNDI in those children. Future research is needed to determine what interventions and support to benefit those children at risk.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".