Stillbirths: Contribution of preterm birth and size‐for‐gestational age for 125.4 million total births from nationwide records in 13 countries, 2000–2020
Bibliographic record
Abstract
OBJECTIVE: To examine the contribution of preterm birth and size-for-gestational age in stillbirths using six 'newborn types'. DESIGN: Population-based multi-country analyses. SETTING: Births collected through routine data systems in 13 countries. SAMPLE: weeks' gestation identified from 2000 to 2020. METHODS: weeks) and size-for-gestational age defined as small (SGA, <10th centile), appropriate (AGA, 10th-90th centiles) or large (LGA, >90th centile) for gestational age, according to the international newborn size for gestational age and sex INTERGROWTH-21st standards. MAIN OUTCOME MEASURES: Distribution of stillbirths, stillbirth rates and rate ratios according to six newborn types. RESULTS: weeks. Most stillbirths (74.3%) were preterm. Around 21.2% were SGA types (PT + SGA [16.2%], PT + AGA [48.3%], T + SGA [5.0%]) and 14.1% were LGA types (PT + LGA [9.9%], T + LGA [4.2%]). The median rate ratio (RR) for stillbirth was highest in PT + SGA babies (RR 81.1, interquartile range [IQR], 68.8-118.8) followed by PT + AGA (RR 25.0, IQR, 20.0-34.3), PT + LGA (RR 25.9, IQR, 13.8-28.7) and T + SGA (RR 5.6, IQR, 5.1-6.0) compared with T + AGA. Stillbirth rate ratios were similar for T + LGA versus T + AGA (RR 0.7, IQR, 0.7-1.1). At the population level, 25% of stillbirths were attributable to small-for-gestational-age. CONCLUSIONS: In these high-quality data from high/middle income countries, almost three-quarters of stillbirths were born preterm and a fifth small-for-gestational age, with the highest stillbirth rates associated with the coexistence of preterm and SGA. Further analyses are needed to better understand patterns of gestation-specific risk in these populations, as well as patterns in lower-income contexts, especially those with higher rates of intrapartum stillbirth and SGA.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".