Androgenic and estrogenic indices in human newborns and infants: the MIREC-ID study
Bibliographic record
Abstract
Prenatal sex steroid exposure plays an important role in determining child development. Yet, measurement of prenatal hormonal exposure has been limited by the paucity of newborn/infant data and the invasiveness of fetal hormonal sampling. Here we provide descriptive data from the MIREC-ID study (n=173 girls; 162 boys) on a range of minimally invasive physical indices thought to reflect prenatal exposure to androgens [anogenital distances (AGDs); penile length/width, scrotal/vulvar pigmentation], to estrogens [vaginal maturation index (VMI) - the degree of maturation of vaginal wall cells] or to both androgens/estrogens [2nd-to-4th digit ratio (2D:4D); areolar pigmentation, triceps/sub-scapular skinfold thickness, arm circumference]. VMI was found to be associated with triceps skinfold thickness (β=0.265, P=0.005), suggesting that this marker may be sensitive to estrogen levels produced by adipose tissue in girls. Both estrogenic and androgenic markers (VMI: β=0.338, P=0.031; 2D:4D - right: β=-0.207, P=0.040; left: β=-0.276, P=0.006; AGD-fourchette - β=0.253, P=0.036) were associated with areolar pigmentation in girls, supporting a role for the latter as an index of both androgen and estrogen exposure. We also found AGD-penis (distance from the anus to the penis) to be associated with scrotal pigmentation (β=0.290, P=0.048), as well as right arm circumference (β=0.462, P<0.0001), supporting the notion that these indices may be used together as markers of androgen exposure in boys. In sum, these findings support the use of several physical indices at birth to convey a more comprehensive picture of prenatal exposure to sex hormones.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".