Modifiable Risk Factors Associated with Clearance of Type-Specific Cervical Human Papillomavirus Infections in a Cohort of University Students
Bibliographic record
Abstract
BACKGROUND: Previous findings regarding risk factors for human papillomavirus (HPV) persistence, other than viral determinants, identified from prospective cohort studies have been inconsistent in part because study designs have differed with respect to differing HPV detection methods and varying lengths of follow-up time. Therefore, the objectives of this study were to continue the search for epidemiologic risk factors of persistent cervical HPV infections and determine what behaviors differed between those women with transient HPV infections and those women who cannot clear their type-specific HPV infections. METHODS: Female university students (n = 621) in Montreal were followed for 24 months at 6-month intervals. At each visit, a cervical cell specimen was collected. HPV DNA was detected using the MY09/MY11 PCR protocol and 27 HPV genotypes were identified by the line blot assay (Roche Molecular Systems, Inc., Alameda, CA). Proportional hazards regression was used to estimate the crude and adjusted hazard ratios of clearing a type-specific high-risk (n = 222) or low-risk (n = 105) HPV infection over time according to specific baseline and time-dependent covariates. RESULTS: Daily consumption of vegetables seemed to increase the rate of HPV clearance independent of type. The use of tampons was associated with a reduced rate of high-risk HPV clearance, whereas regular condom use was associated with an increased rate of low-risk HPV clearance only. CONCLUSION: Some proactive measures can be taken to increase the rate of HPV clearance, and there may be some differences between the sets of predictors of low-risk and high-risk HPV clearance.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".