Viral Load of Human Papillomavirus (HPV) During Pregnancy and Its Association With HPV Vertical Transmission
Bibliographic record
Abstract
Little is known on the dynamics of human papillomavirus (HPV) viral load during pregnancy and on the impact of viral load on HPV vertical transmission. We described viral loads for several genotypes during pregnancy and analyse its association with vertical transmission. Data were analysed from the HERITAGE study, a cohort of pregnant women recruited between 2010 and 2016 in three centres in Canada. Vaginal samples were collected at the first and third trimesters of pregnancy, placental samples were collected at birth, and conjunctival, oral, pharyngeal, and genital samples were collected in children at birth and 3 months were tested for HPV DNA and viral load by Linear Array essay. The association between viral load and vertical transmission was measured using logistic regression. Odd ratios (ORs) and their 95% Confidence intervals (CI) were adjusted for age of the mother. We included women in the cohort infected with the 13 most common genotypes during pregnancy (n = 287). A decrease in HPV viral load was observed during pregnancy (median difference between the third and first trimester of pregnancy = -0.005 copies/cell [p < 0.05]). Women with more than 2 HPV copies/cell (compared to those with ≤ 2 copies) at first trimester had a statistically significant higher risk of vertical transmission (adjusted OR = 6.41; 95% CI: 1.10-37.34 for any genotypes and OR = 17.17; 95% CI: 1.18-250.28 for HPV-16). Viral load values analysed continuously or categorized with different cut-offs showed comparable results. HPV viral load varied during pregnancy and was strongly associated with HPV vertical transmission. The results provide a better understanding of risk factors associated with vertical transmission.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".