The Effect of Birth Order on Neonatal Morbidity and Mortality in Very Preterm Twins
Bibliographic record
Abstract
Objective This retrospective cohort study examined the effect of birth order on neonatal morbidity and mortality in very preterm twins. Study Design Using 2005 to 2012 data from the Canadian Neonatal Network, very preterm twins born between 24 0/7 and 32 6/7 weeks of gestation were included. Odds of morbidity and mortality of second-born cotwins compared with first-born cotwins were examined by matched-pair analysis. Outcomes were neonatal death, severe brain injury (intraventricular hemorrhage grade 3 or 4 or persistent periventricular echogenicity), bronchopulmonary dysplasia, severe retinopathy of prematurity (ROP) (> stage 2), necrotizing enterocolitis (≥ stage 2), and respiratory distress syndrome (RDS). Multivariable analysis was performed adjusting for confounders. Result There were 6,636 twins (3,318 pairs) included with a mean gestational age (GA) of 28.9 weeks. A higher rate of small for GA occurred in second-born twins (10 vs. 6%). Mortality was significantly lower for second-born twins (4.3 vs. 5.3%; adjusted odds ratio: 0.75; 95% confidence interval [CI]: 0.59–0.95). RDS (66 vs. 60%; adjusted odds ratio: 1.40; 95% CI: 1.29–1.52) and severe retinopathy (9 vs. 7%; adjusted odds ratio: 1.46; 95% CI: 1.07–2.01) were significantly higher in second-born twins. Conclusion Thus, while second-born twins had reduced odds of mortality, they also had increased odds of RDS and ROP.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".