Brief Report: Is Gestational Exposure to HIV and Protease Inhibitors Associated With Timing of Pubertal Onset?
Bibliographic record
Abstract
BACKGROUND: Few studies have evaluated the influence of gestational HIV/antiretroviral exposure on pubertal onset in children who are HIV-exposed but uninfected (CHEU). METHODS: CHEU in the Surveillance Monitoring for ART Toxicities study and children HIV-unexposed uninfected (CHUU) in the Bone Mineral Density in Childhood Study with Tanner staging at age 9 years were included. Pubertal onset was defined as Tanner stage ≥2 for each sex-specific puberty indicator. Log-binomial regression models were fit to estimate relative risks (RRs) of pubertal onset in CHEU vs. CHUU, adjusted for exact age and other covariates. Among CHEU, models were fit to assess the association of pubertal onset with maternal protease inhibitor exposure, CD4 count, and earliest HIV viral load (VL) during pregnancy. RESULTS: In total, 227 CHEU (114 female, 113 male) and 344 CHUU (182 female, 162 male) were included. Among male CHEU, the adjusted likelihood of pubertal onset by age 9 years was 2.07 times higher [95% CI: 0.89 to 4.79] for genitalia and 3.55 times higher [95% CI: 0.92 to 13.81] for pubic hair than male CHUU. Pubertal onset was similar in female CHEU and CHUU. Among male CHEU, a maternal VL ≥400 copies/mL was associated with a greater likelihood of pubertal onset (adjusted RR: 12.6 [95% CI: 1.56 to 102] for genitalia and 9.85 [95% CI: 1.17 to 83] for pubic hair). CONCLUSIONS: Gestational HIV exposure and exposure to higher maternal HIV viral load were associated with greater likelihood of pubertal onset by age 9 years in male CHEU. Further confirmatory and mechanistic studies are warranted.
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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 | 0.011 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".