Prenatal versus postnatal sex steroid hormone effects on autistic traits in children at 18 to 24 months of age
Bibliographic record
Abstract
BACKGROUND: Studies of prenatal exposure to sex steroid hormones predict autistic traits in children at 18 to 24 and at 96 months of age. However, it is not known whether postnatal exposure to these hormones has a similar effect. This study compares prenatal and postnatal sex steroid hormone levels in relation to autistic traits in 18 to 24-month-old children.Fetal testosterone (fT) and fetal estradiol (fE) levels were measured in amniotic fluid from pregnant women (n = 35) following routine second-trimester amniocentesis. Saliva samples were collected from these children when they reached three to four months of age and were analyzed for postnatal testosterone (pT) levels. Mothers were asked to complete the Quantitative Checklist for Autism in Toddlers (Q-CHAT), a measure of autistic traits in children 18 to 24 months old. FINDING: fT (but not pT) levels were positively associated with scores on the Q-CHAT. fE and pT levels showed no sex differences and no relationships with fT levels. fT levels were the only variable that predicted Q-CHAT scores. CONCLUSIONS: These preliminary findings are consistent with the hypothesis that prenatal (but not postnatal) androgen exposure, coinciding with the critical period for sexual differentiation of the brain, is associated with the development of autistic traits in 18 to 24 month old toddlers. However, it is recognized that further work with a larger sample population is needed before the effects of postnatal androgen exposure on autistic traits can be ruled out. These results are also in line with the fetal androgen theory of autism, which suggests that prenatal, organizational effects of androgen hormones influence the development of autistic traits in later life.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".