Human papillomavirus (HPV) perinatal transmission and risk of HPV persistence among children: Design, methods and preliminary results of the HERITAGE study
Bibliographic record
Abstract
Perinatal route of transmission of human papillomavirus (HPV) has been demonstrated in several small studies. We designed a large prospective cohort study (HERITAGE) to better understand perinatal HPV. The objective of this article is to present the study design and preliminary data. In the first phase of the study, we recruited 167 women in Montreal, Canada, during the first trimester of pregnancy. An additional 850 are currently being recruited in the ongoing phase. Cervicovaginal samples were obtained from mothers in the first trimester and tested for HPV DNA from 36 mucosal genotypes (and repeated in the third trimester for HPV-positive mothers). Placental samples were also taken for HPV DNA testing. Conjunctival, oral, pharyngeal and genital samples were collected for HPV DNA testing in children of HPV-positive mothers at every 3-6 months from birth until 2 years of age. Blood samples were collected in mother and children for HPV serology testing. We found a high prevalence of HPV in pregnant women (45%[95%CI:37-53%]) and in placentas (14%[8-21%]). The proportion of HPV positivity (any site) among children at birth/3-months was 11%[5-22%]. HPV was detected in children in multiple sites including the conjunctiva (5%[10-14%]). The ongoing HERITAGE cohort will help provide a better understanding of perinatal HPV.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.001 |
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".