Keep it in the family: comparing perinatal risks in small-for-gestational-age infants based on population vs within-sibling designs
Bibliographic record
Abstract
BACKGROUND: Small-for-gestational-age (SGA) birth is commonly used as a proxy for fetal growth restriction, but also includes constitutionally small infants. Genetic factors account for almost half of the risk of SGA birth. We estimated perinatal risks of SGA birth using both population-based and within-sibling analyses, where the latter by design controls for shared genetic factors and maternal environmental factors that are constant across pregnancies. METHODS: This was a prospective nationwide cohort study of 2 616 974 singleton infants born in Sweden between January 1987 and December 2012, of whom 1 885 924 were full siblings. We estimated associations between severe or moderate SGA (<3rd percentile and 3rd to <10th percentiles, respectively) and risks of stillbirth, neonatal mortality and morbidity, using both population-based and within-sibling analyses. Hazard ratios (HRs) with 95% confidence intervals (CIs) were estimated in stillbirth analyses, whereas relative risks (RRs) were used for analyses of neonatal outcomes. RESULTS: Compared with non-SGA births (>10th percentile), the HR (95% CI) of stillbirth was 18.5 (95% CI 17.4-19.5) among severe SGA births in the population analysis and 22.5 (95% CI 18.7-27.1) in the within-sibling analysis. In non-malformed infants, RRs for neonatal mortality in moderate and severe SGA infants were similarly increased in both population and within-sibling analyses. In term non-malformed infants (≥37 weeks), SGA-related RRs of several neonatal morbidities were higher in within-sibling than in population analyses. CONCLUSIONS: Perinatal risks associated with fetal growth restriction are more accurately estimated from analyses of SGA in which genetic factors are accounted for.
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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 | 0.022 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".