Distribution of HPV genotypes among women with abnormal cytology results in Alberta, Canada
Bibliographic record
Abstract
Background: Persistent infection with a subset of human papillomavirus (HPV) genotypes can cause abnormal cytology and invasive cervical cancer. This study examines the circulating HPV genotype strains in a local population of the province of Alberta (a largely unvaccinated population) to establish baseline frequency of vaccine and non-vaccine genotypes causing abnormal cervical cytology. Method: Remnant liquid-based cytology specimens from the Alberta Cervical Cancer Screening Program (March 2014–January 2016) were examined. Only specimens from women who had a cytology grading of atypical squamous cells of undetermined significance or higher were included. HPV genotype was determined for all samples, and results were stratified by demographics and cytology results. Results: Forty-four unique HPV genotypes were identified from 4,794 samples. Of the 4,241 samples with a genotype identified, the most common genotypes were HPV 16, 18, 31, and 51, with 1,599 (37.7%), 441 (12.2%), 329 (7.8%), and 354 (8.4%), respectively. HPV9 vaccine genotypes made up 73.2% of these genotyped samples. Compared with specimens in which HPV9 vaccine genotypes were not detected, those with a genotype covered by the HPV9 vaccine were from younger women (33 [interquartile range {IQR] 28 to 42] y versus 40 [IQR 32 to 51] y; p < 0.00001). Conclusions: The baseline distribution of HPV genotypes in this largely unvaccinated population indicates that the HPV9 vaccine provides good protection from high-risk HPV infections. Determining the frequency of genotypes causing abnormal cytology in this population post–vaccine implementation will be important to assess efficacy of vaccination and monitor for any potential genotype replacement.
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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 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".