Prepubertal organochlorine pesticide concentrations and age of pubertal onset among Russian boys
Bibliographic record
Abstract
BACKGROUND: In animal studies, organochlorine pesticide (OCP) exposure alters pubertal development; however, epidemiological data are limited and inconsistent. OBJECTIVE: To evaluate the associations of serum OCP concentrations [hexachlorobenzene (HCB), β-hexachlorocyclohexane (β-HCH), and p,p'-dichlorodiphenyldichloroethylene (p,p'-DDE)] with male pubertal onset. METHODS: In Chapaevsk, Russia, a town environmentally contaminated with OCPs, 350 8-9 year old boys with measured OCPs were enrolled during 2003-2005 and were followed annually for eight years. We evaluated three measures of pubertal onset: testicular volume (TV)>3 mL in either testis, or stage 2 or greater for genitalia (G2+), or pubic hair (P2+). We used multivariable interval-censored models to evaluate associations of OCPs (quartiles) with physician-assessed pubertal onset. RESULTS: In adjusted models, boys with higher HCB concentrations had later mean ages of TV>3 mL and P2+ (but not G2+). Mean age at attaining TV>3 mL was delayed 3.6 (95% CI: -2.6, 9.7), 7.9 (95% CI: 1.7, 14.0), and 4.7 months (95% CI: -1.4, 10.9) for HCB Q2, Q3, and Q4, respectively, compared to Q1 (trend p: 0.06). Boys with higher HCB concentrations reached P2+ 0.1 months earlier (95% CI: -5.8, 5.6) for Q2, 4.7 months later (95% CI: -1.0, 10.3) for Q3 and 4.6 months later (95% CI: -1.1, 10.3) for Q4 compared to Q1 (trend p: 0.04). There were no associations of serum β-HCH and p,p'-DDE concentrations with age of pubertal onset. CONCLUSION: Higher prepubertal serum HCB concentrations were associated with later age of gonadarche and pubarche.
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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 | 0.000 | 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) | 0.001 | 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".