Mortality in Infants Affected by Preterm Birth and Severe Small-for-Gestational Age Birth Weight
Bibliographic record
Abstract
BACKGROUND: Few researchers have evaluated neonatal mortality in the combined presence of preterm birth (PTB) and small-for-gestational age (SGA) birth weight. None differentiated between infants with and without anomalies, considered births starting at 23 weeks' gestation, or defined SGA at a more pathologic cutpoint less than the fifth percentile. METHODS: We completed a population-based cohort study within the province of Ontario, Canada, from 2002 to 2015. Included were 1 676 110 singleton hospital live births of 23 to 42 weeks' gestation. Modified Poisson regression compared rates and relative risks of neonatal mortality among those with (1) preterm birth at 23 to 36 weeks' gestation and concomitant severe small for gestational age (PTB-SGA), (2) PTB at 23 to 36 weeks' gestation without severe SGA, (3) term birth with severe SGA, and each relative to (4) neither. Relative risks were adjusted for maternal age and stratified by several demographic variables. RESULTS: Relative to a neonatal mortality rate of 0.6 per 1000 term infants without severe SGA, the rate was 2.8 per 1000 among term births with severe SGA (adjusted relative risk [aRR] 4.6; 95% confidence interval [CI] 4.0-5.4), 22.9 per 1000 for PTB without severe SGA (aRR 38.3; 95% CI 35.4-41.4) and 60.0 per 1000 for PTB-SGA (aRR 96.7; 95% CI 85.4-109.5). Stratification by demographic factors showed a persistence of this pattern of neonatal death. Restricting the sample to births at ≥24 weeks' gestation, or newborns without a congenital or chromosomal anomaly, also demonstrated the same pattern. CONCLUSIONS: Methods to detect or prevent PTB or SGA should focus on PTB-SGA, which serves as a useful perinatal surveillance indicator.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".