Patterns of infant mortality caused by major congenital anomalies
Bibliographic record
Abstract
BACKGROUND: We assessed the impact of recent advances in perinatal care on infant mortality due to congenital anomaly. METHODS: Analysis of trends in congenital anomaly-attributed infant mortality, using the 1981-1995 Statistics Canada's birth and death records, with a total of 2,878,826 live births, 21,883 infant deaths, and 6, 908 infant deaths due to congenital anomalies. RESULTS: Infant mortality due to major congenital anomaly decreased from 3.11 per 1, 000 live births in 1981 to 1.89 per 1,000 live births in 1995. Cause-specific infant mortality rates for anencephaly, spina bifida, other central nervous system anomalies, cardiovascular system anomalies, respiratory system anomalies, digestive system anomalies, certain musculoskeleton anomalies, urinary system anomalies, chromosomal anomalies, and multiple congenital anomalies were 0.20, 0.23, 0.27, 1.04, 0.24, 0.08, 0.22, 0.16, 0.22, and 0.13 per 1,000 live births, respectively, in 1981-1983, whereas corresponding rates were 0.07, 0.07, 0.18, 0.73, 0.25, 0.03, 0.12, 0.12, 0.26, and 0.06 per 1,000 live births, respectively, in 1993-1995. CONCLUSIONS: Recent Canadian data show that infant deaths caused by major congenital anomalies have decreased significantly, but reductions varied substantially according to specific forms of anomalies.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".