Serologic response to human papillomavirus genotypes following vaccination: findings from the HITCH cohort study
Bibliographic record
Abstract
Human papillomavirus (HPV) infection contributes to approximately 5% of the worldwide cancer burden. The three-dose HPV vaccine has demonstrated immunogenicity and efficacy. Humoral responses may be critical for preventing, controlling, and/or eliminating HPV infection. Using data from the HITCH cohort, we analysed humoral immune response to HPV vaccination among women in relation to the phylogenetic relatedness of HPV genotypes. We included 96 women aged 18–24 years attending college or university in Montreal, Canada. Participants provided blood samples at enrolment and five follow-up visits. Antibody response to bacterially expressed L1 and E6 glutathione S‐transferase fusion proteins of multiple Alphapapillomavirus types, and to virus-like particles (VLP-L1) of HPV16 and HPV18 were measured using multiplex serology. We assessed correlations between antibody seroreactivities using Pearson correlations (r). At enrolment, 87.7% of participants were unvaccinated, 2.4% had received one, 3.2% two, and 6.7% three doses of HPV vaccine. The corresponding L1 seropositivity to any HPV was 41.2%, 83.3%, 100%, and 97.0%. Between-type correlations for L1 seroreactivities increased with the number of vaccine doses, from one to three. Among the latter, the strongest correlations were observed for HPV58–HPV33 (Pearson correlation [r] = 0.96; α9-species); HPV11–HPV6 (r = 0.96; α10-species); HPV45–HPV18 (r = 0.95; α7-species), and HPV68–HPV59 (r = 0.95; α7-species). Correlations between HPV-specific antibody seroreactivities are affected by phylogenetic relatedness, with anti-L1 correlations becoming stronger with the number of vaccine doses received.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".