Grade 7 School Performance of Children Born Preterm: A Retrospective Canadian Cohort Study
Bibliographic record
Abstract
Abstract Importance: Data on the middle school outcomes of preterm children are limited and have methodologic issues. Objective To study the association between preterm birth and grade 7 school performance. Methods A retrospective population-based cohort study of children born in Manitoba, Canada between 1994–2006 using their grade 7 school performance data. A secondary sibling cohort was created comprising children born preterm and their full-term siblings. Primary exposure was preterm birth categorized as < 28, 28–33 and 34–36 weeks gestation. The two co-primary grade 7 outcome measures were: not meeting the mathematics competencies, and not meeting the student engagement competencies. Multivariable logistic regression models tested the association between preterm birth and both co-primary outcomes; adjusted odds ratios (aORs) and 95% confidence intervals (CIs) were calculated. Results 7653 preterm (gestational age median [IQR]: 35 weeks [34,36]) and 110,313 term (40 [39,40]) were included. 43% of < 28 weeks, 18% of 28–33 weeks and 17% of 34–36 weeks had the mathematics co-primary outcome compared to 13% of term children. The corresponding % for the student engagement outcome were 42%, 24%, 24% and 24% respectively. Preterm birth was associated with the mathematics (34–36 weeks: 1.26, 1.16–1.35; 28–33 weeks: 1.47, 1.27–1.70; <28 weeks: 5.48, 3.89–7.70) and student engagement outcomes (34–36 weeks: 1.09, 1.01–1.16; 28–33 weeks: 1.21, 1.06–1.39; <28 weeks: 2.49, 1.76–3.51). However, there was no difference in outcomes among the sibling cohort. Conclusions and Relevance: Children born preterm had lower grade 7 performance compared to children born term in this population-based cohort. Screening and supports for them in their middle school years are warranted.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".