Distribution of human papillomavirus types, cervical cancer screening history, and risk factors for infection in Manitoba
Bibliographic record
Abstract
OBJECTIVES: We conducted a study to investigate the prevalence of human papillomavirus (HPV) infections in an opportunistic sample of women in Manitoba, Canada. We inquired about risk factors associated with HPV infections and linked the HPV typing results with the cervical cancer screening history of the participants. METHODS: The study population included 592 women attending Papanicolaou (Pap) test clinics. After signing a consent form, participants were given a self-administered questionnaire on risk factors and received a conventional Pap test. Residual cells from the Pap tests were collected and sent for HPV typing. RESULTS: The mean age of the population was 43 years. A total of 115 participants (19.4%) had an HPV infection, 89 of whom had a normal Pap test. Of those who were HPV-positive, 61 (10.3%) had high-risk (Group 1) HPV. HPV-16 was the most prevalent type (15/115: 13.0% of infections). The most consistent risk factors for HPV infection were young age, Aboriginal ethnicity, higher lifetime number of sexual partners and higher number of sexual partners in the previous year. CONCLUSION: The prevalence of HPV types in Manitoba is consistent with the distributions reported in other jurisdictions. These data provide baseline information on type-specific HPV prevalence in an unvaccinated population and can be useful in evaluating the effectiveness of the HPV immunization program. An added benefit is in the validation of a proof of concept which links a population-based Pap registry to laboratory test results and a risk behaviour survey to assess early and late outcomes of HPV infection. This methodology could be applied to other jurisdictions across Canada where such capacities exist.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".