Small head circumference at birth: an 8-year retrospective cohort study in China
Bibliographic record
Abstract
OBJECTIVE: Head circumference is considered a reliable assessment of the volume of the underlying brain. We sought to identify risk factors (maternal factors or antenatal antecedents) for microcephaly and to assess the effects of microcephaly on neonatal outcomes. DESIGN: Retrospective cohort study. SETTING: Data for all births in 2009-2017 were obtained from the Guangzhou Maternal-Fetal Care Database. PARTICIPANTS: All singleton liveborn infants between 33 and 42 weeks' gestation (n=45 663) were categorised using the Intergrowth-21st standard for microcephaly. MAIN OUTCOME MEASURES: , rubella virus, cytomegalovirus, herpes simplex virus), in vitro fertilisation conception, pre-eclampsia and maternal congenital anomalies with risk of each category of microcephaly, and (2) microcephaly with risk of in-hospital mortality and severe morbidity. RESULTS: A total of 2709 infants had a head circumference z-score >2 SD, resulting in an overall prevalence of microcephaly of 59.3 per 1000 infants, consisting of mild (54.1 per 1000), absolute (2.8 per 1000) and relative microcephaly (2.4 per 1000). In multiple logistic regression, absolute microcephaly was associated with in utero exposure to teratogens (OR 4.2, 95% CI 2.0 to 8.8) and TORCH agents (OR 3.2, 95% CI 1.1 to 9.5). Mild microcephaly was associated with Cantonese descent (OR) 1.5, 95% CI 1.3 to 1.7) and primiparity (OR 1.7, 95% CI 1.5 to 2.0). Absolute microcephaly was associated with a significantly higher odds of neonatal seizure (OR 8.7, 95% CI 1.1 to 69.1). Mild microcephaly was not associated with adverse neonatal outcomes overall. CONCLUSIONS: Cantonese origin, exposure to teratogens, pre-eclampsia and TORCH infection may be risk factors for microcephaly. The high prevalence of relative microcephaly and associated poor outcomes suggests that high-risk women merit closer clinical management and follow-up to maximise fetal head development during pregnancy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".