Canadian oncogenic human papillomavirus cervical infection prevalence: Systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Oncogenic human papillomavirus (HPV) infection prevalence is required to determine optimal vaccination strategies. We systematically reviewed the prevalence of oncogenic cervical HPV infection among Canadian females prior to immunization. METHODS: We included studies reporting DNA-confirmed oncogenic HPV prevalence estimates among Canadian females identified through searching electronic databases (e.g., MEDLINE) and public health websites. Two independent reviewers screened literature results, abstracted data and appraised study quality. Prevalence estimates were meta-analyzed among routine screening populations, HPV-positive, and by cytology/histology results. RESULTS: Thirty studies plus 21 companion reports were included after screening 837 citations and 120 full-text articles. Many of the studies did not address non-response bias (74%) or use a representative sampling strategy (53%). Age-specific prevalence was highest among females aged < 20 years and slowly declined with increasing age. Across all populations, the highest prevalence estimates from the meta-analyses were observed for HPV types 16 (routine screening populations, 8 studies: 8.6% [95% confidence interval 6.5-10.7%]; HPV-infected, 9 studies: 43.5% [28.7-58.2%]; confirmed cervical cancer, 3 studies: 48.8% [34.0-63.6%]) and 18 (routine screening populations, 8 studies: 3.3% [1.5-5.1%]; HPV-infected, 9 studies: 13.6% [6.1-21.1%], confirmed cervical cancer, 4 studies: 17.1% [6.4-27.9%]. CONCLUSION: Our results support vaccinating females < 20 years of age, along with targeted vaccination of some groups (e.g., under-screened populations). The highest prevalence occurred among HPV types 16 and 18, contributing a combined cervical cancer prevalence of 65.9%. Further cancer protection is expected from cross-protection of non-vaccine HPV types. Poor study quality and heterogeneity suggests that high-quality studies are needed.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 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".