Preterm birth, stillbirth and infant mortality among triplet births in Canada, 1985–96
Bibliographic record
Abstract
Recent increases in the frequency of multiple births and simultaneous increases in preterm birth among multiple births have focused attention on such births. However, most previous studies have examined twins rather than higher-order multiples. We carried out a study to examine rates and trends in preterm birth and in gestational age-specific fetal and infant mortality among triplet births in Canada. We used data from the stillbirth, live birth and mortality files of Statistics Canada for the years 1985-97. All births in Canada (excluding those occurring in Ontario and Newfoundland) were included in the study, with two periods (1985-90 vs. 1991-96) being contrasted for assessing temporal change. Changes were estimated using relative risks, 95 confidence intervals [CI] and two-tailed P-values. The rate of preterm birth among triplet live births increased by 6 (95 CI 3, 9) from 90.4 in 1985-90 to 96.0 in 1991-96. Stillbirth rates among triplets did not change significantly and were 30.3 per 1000 total births in 1985-90 and 33.8 per 1000 total births in 1991-96. Infant mortality among triplets declined from 112.7 per 1000 live births in 1985-90 to 73.8 per 1000 live births in 1991-96. In spite of temporal reductions in infant mortality, triplet births continue to be associated with very high rates of preterm birth and fetal and infant mortality. Fetal mortality among triplets has not changed over the last ten years.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".