Abortion and Infant Mortality on the First Day of Life
Bibliographic record
Abstract
BACKGROUND: Fetal imaging for congenital anomalies increases pregnancy terminations late in gestation. OBJECTIVES: We assessed whether late-pregnancy terminations can accidentally result in live births, and how these births impact infant mortality rates over time. METHODS: We carried out a population-level analysis of 12,141 infant deaths in Quebec, Canada from 1986 to 2012. We calculated the proportion of infants born alive who died following pregnancy termination. The exposure was pregnancy termination with or without congenital anomaly recorded on death certificates. The main outcome was mortality on the first day of life by the hour. RESULTS: Pregnancy termination was the cause of 19.4 infant deaths per 100,000 in 2000-2012, compared with 1.0 per 100,000 in 1986-1999. Most deaths after termination occurred in the first 3 h of life among infants with anomalies who weighed <500 g. In 2000-2012, infants who died following pregnancy termination led to an excess of 0.2 deaths per 1,000 on the first day of life, i.e. an 8.6% increase in the infant mortality rate (p value = 0.002). CONCLUSIONS: Pregnancy termination in mid-gestation carries the risk of accidental live birth. These neonates increasingly affect infant mortality rates. Better recording is needed, including data on the prevention and management of accidental live births after pregnancy termination.
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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 | 0.004 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".