Perinatal Mortality in Term and Preterm Twin and Singleton Births
Bibliographic record
Abstract
Abstract Although, in general, twins have higher perinatal mortality rates than singletons, preterm twins have lower perinatal mortality rates than singletons of the same birth weight or gestational age. This study investigated the hypotheses that this paradoxical twin advantage: 1) is due to gestational age distribution differences between the singleton and twin populations, and 2) is due to increased likelihood of birth having occurred in a tertiary perinatal center. A pre-existing, time-limited data set of all births in the province of Ontario in odd years between 1979 and 1985 was chosen for this study because of the large sample size (n = 618,579). Multivariable logistic regression of the relationship between perinatal mortality and twin status was controlled for mother’s age, hospital level and gestational age. Findings confirm the lower mortality of preterm twins. After controlling for level of hospital of birth this difference remained, suggesting that level of hospital of birth was not a major factor responsible for the twin advantage. Analyses in which gestational age was standardized indicate that, for those whose gestational age was less than 2 SD below the mean for their particular group (twin or singleton), twins were actually at higher risk than singletons. These results support hypothesis 1 and do not strongly support hypothesis 2. The results also support earlier authors’ suggestions that the definition of term birth should be different for twins and singletons
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".