Prevalence and Subtype Distribution of High-Risk Human Papillomavirus Among Women Presenting for Cervical Cancer Screening at Karanda Mission Hospital
Bibliographic record
Abstract
PURPOSE High-risk human papillomaviruses (hrHPV) are the primary cause of cervical cancer. Human papillomavirus (HPV) vaccination is expected to prevent cervical cancers caused by the HPV types included in vaccines and possibly by cross-protection from other types. This study sought to determine the hrHPV type distribution in women at a rural Zimbabwe hospital. METHODS We implemented a cross-sectional study at the Karanda Mission Hospital. Using the Visual Inspection with Acetic Acid Cervicography technique, clinicians collected cervical swabs from 400 women presenting for screening for cervical cancer. Samples were initially analyzed by Cepheid GeneXpert; candidate hrHPV genotypes were further characterized using the Anyplex II HPV28 Detection Kit. RESULTS Twenty-one percent of the 400 women were positive for a high-risk genotype when using the GeneXpert analyzer; 17% were positive when using the multiplex analysis. Almost two thirds of the hrHPV women had a single DNA type identified, whereas one third had multiple genotypes, ranging from 2 to 5. hrHPV was observed more frequently in HIV-positive than in HIV-negative women (27% v 15%). Of the 113 isolates obtained, 77% were hrHPV genotypes not included in the bivalent or quadrivalent vaccines, and 47% represented DNA types not covered in the nonavalent vaccine. Forty-seven percent of the women with hrHPV harbored a single genotype that was not covered by the nonavalent vaccine. CONCLUSION A large fraction of hrHPV isolates from women participating in a cervical cancer screening program in northern Zimbabwe are DNA types not covered by the bivalent, quadrivalent, or nonavalent vaccines. These findings suggest the importance of characterizing the hrHPV DNA types isolated from cervical neoplasia in this population and determining whether cross-immunization against these genotypes develops after administration of the vaccines in current use.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.001 | 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 teacher head, 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".