Distribution of Vaccine-Type Human Papillomavirus Does Not Differ by Race or Ethnicity Among Unvaccinated Young Women
Bibliographic record
Abstract
BACKGROUND: Previous studies have demonstrated racial and ethnic differences in the distribution of human papillomavirus (HPV) types among adult women with cervical precancers. The aim of this study was to determine whether the distribution of vaccine-targeted HPV types varies by race/ethnicity among unvaccinated young women. MATERIALS AND METHODS: A secondary analysis was performed using data from four studies of sexually experienced, unvaccinated, 13-26-year-old women. Participants completed surveys and provided a cervicovaginal swab for HPV DNA testing. Multivariable logistic regression analyses were performed to examine whether race, ethnicity, and other factors were associated with type-specific HPV infection among the overall sample and among HPV-infected participants. Models controlled for age, HPV knowledge, sexual behaviors, substance use, and random study effect. RESULTS: The mean age of participants (N = 841) was 19.3 years; 64.4% were black and 8.9% Hispanic. Black women were more likely than white women to be positive for ≥1 HPV type (odds ratio [OR] 1.83, 95% CI 1.30-2.58) and Hispanic women were less likely than non-Hispanic women to be positive for ≥1 HPV type (OR 0.47, 95% CI 0.24-0.92). However, among all young women and HPV-infected women, neither race nor ethnicity was associated with positivity for HPV types targeted by the following vaccines: 2-valent (HPV16 and/or 18), 4-valent (HPV6, 11, 16, and/or 18), or 9-valent (HPV6, 11, 16, 18, 31, 33, 45, 52, and/or 58). CONCLUSION: The prevalence of HPV types targeted by the 2-valent, 4-valent, and 9-valent vaccines did not differ by race or ethnicity among all and among HPV-infected women in this sample.
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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 | 0.000 | 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) | 0.002 | 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".