Distribution of human papillomavirus genotypes in cervical intraepithelial neoplasia and invasive cervical cancer in Canada
Bibliographic record
Abstract
Infection with high-risk human papillomavirus (HPV) causes cervical intraepithelial neoplasia (CIN) and invasive cervical cancer (ICC). The distribution of HPV types in cervical diseases has been previously described in small studies for Canadian women. The prevalence of 36 HPV genotypes in 873 women with CIN and 252 women with ICC was assessed on cervical exfoliated cells analyzed with the Linear Array (Roche Molecular System). HPV16 was the most common genotype in CIN and ICC. The seven most frequent genotypes in order of decreasing frequency were HPV16, 51, 52, 31, 39, 18, and 56 in women with CIN1, HPV16, 52, 31, 18, 51, 39, and 33 in women with CIN2, HPV16, 31, 18, 52, 39, 33, and 58 in women with CIN3, and HPV16, 18, 45, 33, 31, 39, and 53 in women with ICC. HPV18 was detected more frequently in adenocarcinoma than squamous cell carcinoma (P = 0.013). Adjustment for multiple type infections resulted in a lower percentage attribution in CIN of HPV types other than 16 or 18. The proportion of samples containing at least one oncogenic type was greater in CIN2 (98.4%) or CIN3 (100%) than in CIN1 (80.1%; P < 0.001 for each comparison). Multiple type infections were demonstrated in 51 (20.2%) of 252 ICC in contrast to 146 (61.3%) of 238 women with CIN3 (P < 0.001). Adjusting for multiple HPV types, HPV16 accounted for 52.1% and HPV18 for 18.1% of ICCs, for a total of 70.2%. Current HPV vaccines should protect against HPV types responsible for 70% of ICCs in Canadian women.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".