Investigating fetal growth restriction and perinatal risks in appropriate for gestational age infants: using cohort and within‐sibling analyses
Bibliographic record
Abstract
OBJECTIVE: Fetal growth restriction refers to fetuses that fail to reach their growth potential. Studies within siblings may be useful to disclose fetal growth restriction in appropriate for gestational age (AGA) infants. We analysed associations between birthweight percentiles and perinatal risks in AGA infants, using both population-based and within-sibling analyses. DESIGN: Population-based cohort study. SETTING AND SAMPLE: Using nation-wide Swedish registries (1987-2012), we identified 2 134 924 singleton AGA births (10th-90th birthweight percentile for gestational age), of whom 1 377 326 were full siblings. METHODS: Unconditional Poisson regression was used for population analyses, and conditional (matched) Poisson regression for within-sibling analyses. We estimated associations between birthweight percentiles and stillbirth, neonatal mortality, and morbidity, using incidence rate ratios (IRRs) with 95% confidence intervals (CIs). RESULTS: Stillbirth and neonatal mortality risks declined with increasing birthweight percentiles, but the declines were larger in within-sibling analyses. Compared with the reference group (40th to <60th percentile), IRRs (95% CIs) of stillbirth for the lowest and highest percentile groups (10th to <25th and 75th-90th percentiles, respectively) were 1.87 (1.72-2.03) to 0.76 (0.68-0.85) in population analysis and 2.60 (2.27-2.98) and 0.43 (0.36-0.50) in within-sibling analysis. Neonatal morbidity risks in term non-malformed infants with low birthweight percentiles were generally only increased in within-sibling analyses. CONCLUSION: Using birthweight information from siblings may help to define fetal growth restriction in AGA infants. TWEETABLE ABSTRACT: Size of siblings helps to detect growth-restricted infants with seemingly normal birthweights.
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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 | 0.014 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".