Birth weight and head circumference for 22–29 weeks gestation neonates from an international cohort
Bibliographic record
Abstract
OBJECTIVE: Size at birth is a key indicator of in utero growth. Our objective was to generate sex-specific percentiles for birth weight and head circumference in neonates born between 22 and 29 weeks gestation from pregnancies without hypertension or diabetes and assess differences between vaginal and caesarean births and between singletons and twins. METHODS: We used data from 12 countries participating in the International Network for Evaluating Outcomes in Neonates database from 2007 to 2021. We excluded data that were influenced by truncation with 1500g birth weight cut-offs in databases and neonates with major congenital anomalies or born to mothers with hypertension or diabetes. RESULTS: After exclusions, 132 727 neonates contributed to birth weight and 65 406 contributed to head circumference. The percentiles of birth weight were similar between countries at the 50th and 90th percentiles, though variability was noted in the lower percentiles from countries with smaller sample sizes. Head circumference percentiles were comparable between countries. Caesarean births had birth weights similar to vaginal births until 26 weeks after which the weight at 10th percentile diverged by approximately 239 g at 29 weeks. Vaginal births had birth weights very similar to Hadlock's intrauterine estimated fetal weights. There were no differences in head circumference between vaginal and caesarean births and between singletons and twins. CONCLUSIONS: We present updated information on weight and head circumference at birth for preterm neonates of 22-29 weeks gestation born to mothers without hypertension or diabetes derived from a large multicountry cohort. Research is needed to explore the predictive value of these birth size data for health and developmental outcomes.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".