Neonatal mortality risk of large-for-gestational-age and macrosomic live births in 15 countries, including 115.6 million nationwide linked records, 2000–2020
Bibliographic record
Abstract
Objective: We aimed to compare the prevalence and neonatal mortality associated with large for gestational age (LGA) and macrosomia among 115.6 million live births in 15 countries, between 2000 and 2020. Design: Population-based, multi-country study. Setting: National healthcare systems. Population: Liveborn infants. Methods: We used individual-level data identified for the Vulnerable Newborn Measurement Collaboration. We calculated the prevalence and relative risk (RR) of neonatal mortality among live births born at term + LGA (>90th centile, and also >95th and >97th centiles when the data were available) versus term + appropriate for gestational age (AGA, 10th–90th centiles) and macrosomic (≥4000, ≥4500 and ≥5000 g, regardless of gestational age) versus 2500–3999 g. INTERGROWTH 21st served as the reference population. Main outcome measures: Prevalence and neonatal mortality risks. Results: Large for gestational age was common (median prevalence 18.2%; interquartile range, IQR, 13.5%–22.0%), and overall was associated with a lower neonatal mortality risk compared with AGA (RR 0.83, 95% CI 0.77–0.89). Around one in ten babies were ≥4000 g (median prevalence 9.6% (IQR 6.4%–13.3%), with 1.2% (IQR 0.7%–2.0%) ≥4500 g and with 0.2% (IQR 0.1%–0.2%) ≥5000 g). Overall, macrosomia of ≥4000 g was not associated with increased neonatal mortality risk (RR 0.80, 95% CI 0.69–0.94); however, a higher risk was observed for birthweights of ≥4500 g (RR 1.52, 95% CI 1.10–2.11) and ≥5000 g (RR 4.54, 95% CI 2.58–7.99), compared with birthweights of 2500–3999 g, with the highest risk observed in the first 7 days of life. Conclusions: In this population, birthweight of ≥4500 g was the most useful marker for early mortality risk in big babies and could be used to guide clinical management decisions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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".