Human papillomavirus seroprevalence and seroconversion following baseline detection of nine human papillomavirus types in young women
Bibliographic record
Abstract
BACKGROUND: Estimates of the humoral immune response to incident human papillomavirus (HPV) infections are limited. METHODS: In this post hoc analysis of 3875 women aged 16-23 years from a 4-valent HPV vaccine trial (NCT00092482), HPV seroprevalence on day 1 was measured with a 9-valent HPV (HPV 6/11/16/18/31/33/45/52/58) competitive Luminex immunoassay and compared with cervical/external genital HPV detection by polymerase chain reaction. In the control group, among women who were HPV DNA‒negative on day 1, seroconversion following initial HPV detection was estimated using Kaplan-Meier methods. RESULTS: Type-specific HPV seropositivity among women with no day 1 cervical/external genital HPV detection was 0.6%-3.6%. Women with any 9-valent HPV (9vHPV) cervical/external genital detection (796/3875; 20.5%) had concordant seropositivity ranging from 13.4% (HPV 45) to 38.5% (HPV 6). Among women in the control group who were negative for all HPV types on day 1, seroconversion by month 30 after initial detection ranged from 29% (HPV 45) to 75% (HPV 16). CONCLUSIONS: Humoral immune response to HPV is variable and dynamic, depending on type-specific exposure. This longitudinal analysis provides insight into the relationship between incident infection and seropositivity. CLINICALTRIALS: gov; NCT00092482 https://clinicaltrials.gov/ct2/show/NCT00092482.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".