Sociodemographic characteristics, sexual behaviour and knowledge about cervical cancer prevention as risk factors for high-risk human papillomavirus infection in Arkhangelsk, North-West Russia
Bibliographic record
Abstract
While sociodemographic predictors of cervical cancer (CC) are well understood, predictors of high-risk (HR) human papillomavirus (HPV) infection have not been fully elucidated. This study explored the HR-HPV infection positivity in relation to sociodemographic, sexual behavior characteristics and knowledge about HPV and CC prevention among women who visited the Arkhangelsk clinical maternity hospital named after Samoylova, Russia. This cross-sectional study was conducted in the city of Arkhangelsk, Northwest Russia. Women who consulted a gynecologist for any reason between 1 January 2015 and 30 April 2015 were residents of Arkhangelsk, 25–65 years of age were included. The Mann–Whitney and Pearson’s χ2 tests were used. To determine the HR-HPV status, we used the Amplisens HPV-DNA test. We used a questionnaire to collect the information on sociodemographic factors. Logistic regression was applied. The prevalence of HR-HPV infection was 16.7% (n = 50). HR-HPV infection was more prevalent in younger women, cohabiting, nulliparae, smokers, having had over three sexual partners and early age of sexual debut. The odds of having a positive HR-HPV status increased by 25% with an annual decrease in the age of sexual debut. Moreover women with one child or more were less likely to have positive HR-HPV status.
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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 | 0.000 |
| 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".