Impact of Corticosteroid-Binding Globulin Deficiency on Pregnancy and Neonatal Sex
Bibliographic record
Abstract
CONTEXT: Plasma corticosteroid-binding globulin (CBG) transports cortisol but high progesterone levels at the maternal-fetal interface can displace cortisol from its steroid-binding site. A secretion-deficient CBG mutant (A51V) in ∼1 of 36 Chinese causes low circulating CBG levels. OBJECTIVE: Assess the implications of a CBG deficiency on pregnancy outcomes. PARTICIPANTS AND DESIGN: From 1978 Chinese women screened at 12-16 weeks' gestation, 50 A51V carriers were identified and 46 were followed with 60 controls throughout pregnancy. Blood samples from another 2051 pregnant women were obtained at term to determine the secondary sex ratio (SSR) of newborns in an extended cohort (n = 101) of A51V mothers. OUTCOME MEASURES AND RESULTS: Among women recruited at 12-16 weeks' gestation, serum CBG increased progressively during pregnancy but was lower (P < .0001) in heterozygous A51V carriers than controls. Two women homozygous for A51V had very low serum CBG but their pregnancies progressed normally. The A51V mothers did not differ from controls in body mass index, gestational age at delivery, duration of parturition, blood pressure, gravidity, infant birth weight and size, or placental weights, and reported no unusual clinical symptoms. Peripheral CBG and progesterone levels correlated (r = 0.459) during first and second trimesters. Progesterone levels were much higher in intervillous blood and correlated (r = 0.637) with CBG levels. A female-skewed SSR in newborns of A51V mothers (0.77) differed (P < .05) from the SSR (1.17) in a reference cohort. CONCLUSIONS: CBG influences progesterone levels in peripheral blood and at the maternal-fetal interface. The female-skewed SSR suggests that male fetal survival is compromised in CBG-deficient mothers.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".