Effect of Consanguinity on Birth Weight for Gestational Age in a Developing Country
Bibliographic record
Abstract
Consanguinity, the marriage between relatives, has been associated with adverse child health outcomes because it increases homozygosity of recessive alleles. The objective of this study was to assess the effect of consanguinity on the birth weight of newborns in Greater Beirut, Lebanon. Cross-sectional data were collected on 10,289 consecutive liveborn singleton newborns admitted to eight hospitals belonging to the National Collaborative Perinatal Neonatal Network during the years 2000 and 2001. Birth weight was modeled by use of the fetal growth ratio, defined as the ratio of the observed birth weight to the median birth weight for gestational age. A mixed-effect multiple linear regression model was used to predict the net effect of first- and second-cousin marriage on the birth weight for gestational age, accounting for within-hospital clustering of data. After controlling for medical and sociodemographic covariates, the authors found a statistically significant negative association between consanguinity and birth weight at each gestational age. No significant difference was observed in the decrease in birth weight between the first- and second-cousin marriages. Overall, consanguinity was associated with a decrease in birth weight for gestational age by 1.8% (beta = -0.018, 95% confidence interval: -0.027, -0.008). The largest effects on fetal growth were seen with lower parity and smoking 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".