Common Childhood Viruses and Pubertal Timing: The LEGACY Girls Study
Bibliographic record
Abstract
Earlier pubertal development is only partially explained by childhood body mass index; the role of other factors, such as childhood infections, is less understood. Using data from the LEGACY Girls Study (North America, 2011-2016), we prospectively examined the associations between childhood viral infections (cytomegalovirus (CMV), Epstein-Barr virus (EBV), herpes simplex virus (HSV) 1, HSV2) and pubertal timing. We measured exposures based on seropositivity in premenarcheal girls (n = 490). Breast and pubic hair development were classified based on mother-reported Tanner Stage (TS) (TS2+ compared with TS1), adjusting for age, body mass index, and sociodemographic factors. The average age at first blood draw was 9.8 years (standard deviation, 1.9 years). The prevalences were 31% CMV+, 37% EBV+, 14% HSV1+, 0.4% HSV2+, and 16% for both CMV+/EBV+ coinfection. CMV+ infection without coinfection was associated with developing breasts an average of 7 months earlier (hazard ratio (HR) = 2.12, 95% confidence interval (CI): 1.32, 3.40). CMV infection without coinfection and HSV1 and/or HSV2 infection were associated with developing pubic hair 9 months later (HR = 0.41, 95% CI: 0.24, 0.71, and HR = 0.42, 95% CI: 0.22, 0.81, respectively). Infection was not associated with menarche. If replicated in larger cohorts with blood collection prior to any breast development, this study supports the hypothesis that childhood infections might play a role in altering pubertal timing.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".