Mathematical performance in childhood and early adult outcomes after very preterm birth: an individual participant data meta‐analysis
Bibliographic record
Abstract
AIM: To investigate the strength of the independent associations of mathematics performance in children born very preterm (<32wks' gestation or <1500g birthweight) with attending postsecondary education and their current employment status in young adulthood. METHOD: We harmonized data from six very preterm birth cohorts from five different countries and carried out one-stage individual participant data meta-analyses (n=954, 52% female) using mixed effects logistic regression models. Mathematics scores at 8 to 11 years of age were z-standardized using contemporary cohort-specific controls. Outcomes included any postsecondary education, and employment/education status in young adulthood. All models were adjusted for year of birth, gestational age, sex, maternal education, and IQ in childhood. RESULTS: Higher mathematics performance in childhood was independently associated with having attended any postsecondary education (odds ratio [OR] per SD increase in mathematics z-score: 1.36 [95% confidence interval {CI}: 1.03, 1.79]) but not with current employment/education status (OR 1.14 per SD increase [95% CI: 0.87, 1.48]). INTERPRETATION: Among populations born very preterm, childhood mathematics performance is important for adult educational attainment, but not for employment status.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.024 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".