Self-Collected Specimens Revealed a Higher Vaccine- and Non-Vaccine-Type Human Papillomavirus Prevalences in a Cross-Sectional Study in Akuse
Bibliographic record
Abstract
BACKGROUND: Population-specific epidemiologic data on human Papillomavirus infection, which are limited in most of the SubSaharan African countries, are necessary for effective cervical cancer prevention. This study aimed to generate population-specific data on human Papillomavirus infections, and determine which of these, self-collected and provider-collected specimens, gives a higher estimate of the prevalence of human Papillomaviruses, including vaccine and non-vaccine-type human Papillomavirus. METHODS: In this cross-sectional study, following a questionnaire-based collection of epidemiological data, self-, and provider-collected specimens, obtained from women 15-65 years of age, were analysed for human Papillomavirus types by a nested-multiplex polymerase chain reaction, and for cervical lesions by Pap testing. HPV data were categorised according to risk type and vaccine types for further analysis. RESULTS: ≤ 0.001). The prevalence of quadrivalent vaccine-type human Papillomaviruses was 12.3% with self-collected specimens, but 6.0% with provider-collected specimens. For the nonavalent vaccine-types, the prevalences were 26.6% and 16.7% respectively. There were multiple infections involving both vaccine-preventable and nonvaccine preventable high-risk human Papillomavirus genotypes. CONCLUSION: The Akuse subdistrict can, therefore, be said to have a high burden of human Papillomavirus infections, which included nonvaccine types, as detected with both self-collected and provider-collected specimens. These imply that self-collection is to be given a higher consideration as a means for a population-based high-risk human Papillomavirus infections burdens assessment/screening. Additionally, even with a successful implementation of the HPV vaccination, if introduced in Ghana, there is still the need to continue with the screening of women.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".