Vaccine-preventable HPV burden and cervical abnormalities in women during 2022–2024 Octobre Rose campaigns in Libreville
Bibliographic record
Abstract
Cervical cancer is a leading cause of mortality in sub-Saharan Africa, largely driven by persistent carcinogenic human papillomavirus (HPV) infection. We conducted a 3-year cross-sectional study of 1,524 women participating in the national “Octobre Rose” campaigns in Libreville, Gabon. Cervical samples were assessed by visual inspection with acetic acid/Lugol iodine (VIA/VILI) and HPV genotyping using BioPerfectus Multiplex Real-Time PCR assay. Overall HPV prevalence was 21.0%, including 18.5% carcinogenic types and 11.0% covered by the nonavalent vaccine. HPV-35 was dominant, while Gardasil-9-targeted genotypes accounted for 63% of carcinogenic infections and the largest attributable fraction of VIA/VILI-detected abnormalities, particularly HPV-16 and HPV-45. HIV and high lifetime sexual exposure were independent predictors of carcinogenic HPV. These findings reveal a substantial burden of vaccine-preventable HPV in Gabon and underscore the high potential impact of Gardasil-9 implementation, while reinforcing the need for continued HPV-based screening to mitigate residual risk of non-vaccine types such as HPV-35. Analyzing HPV prevalence among 1,524 women in Gabon, the authors show that most cervical abnormalities in Gabonese women are linked to vaccine-preventable HPV types, supporting the introduction of Gardasil-9 ® and strengthened HPV screening to reduce cervical cancer in Gabon.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".