Parent-reported health status of preterm survivors in a Canadian cohort
Bibliographic record
Abstract
OBJECTIVES: Health status (HS)/ health-related quality of life measures, completed by self or proxy, are important outcome indicators. Most HS literature on children born preterm includes adolescents and adults with limited data at preschool age. This study aimed to describe parent-reported HS in a large national cohort of extreme preterm children at preschool age and to identify clinical and sociodemographic variables associated with HS. METHODS: Infants born before 29 weeks' gestation between 2009 and 2011 were enrolled in a prospective longitudinal national cohort study through the Canadian Neonatal Network (CNN) and the Canadian Neonatal Follow-Up Network (CNFUN). HS, at 36 months' corrected age (CA), was measured with the Health Status Classification System for Pre-School Children tool completed by parents. Information about HS predictors was extracted from the CNN and CNFUN databases. RESULTS: Of 811 children included, there were 79, 309 and 423 participants in 23-24, 25-26 and 27-28 weeks' gestational age groups, respectively. At 36 months' CA, 78% had a parent-reported health concern, mild in >50% and severe in 7%. Most affected HS attributes were speech (52.1%) and self-care (41.4%). Independent predictors of HS included substance use during pregnancy, infant male sex, Score for Neonatal Acute Physiology-II, bronchopulmonary dysplasia, severe retinopathy of prematurity, caregiver employment and single caregiver. CONCLUSION: Most parents expressed no or mild health concerns for their children at 36 months' CA. Factors associated with health concerns included initial severity of illness, complications of prematurity and social factors.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| 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".