The impact of the distribution of human papillomavirus types and associated high-risk lesions in a colposcopy population for monitoring vaccine efficacy.
Bibliographic record
Abstract
CONTEXT: Impact studies of the new human papillomavirus (HPV) vaccines will be biased unless local baseline distribution studies are conducted. Vaccine cross protection for other important oncogenic HPV types and the emergence of potential genotype replacements require the knowledge of the prevaccine epidemiology of HPV. OBJECTIVE: To determine the prevaccine distribution of HPV types in Saskatchewan, using a subpopulation of women referred to a colposcopy clinic. DESIGN: One thousand three hundred fifty-five specimens obtained during colposcopic examination were typed for HPV using L1 or E1 gene polymerase chain reaction and direct sequencing. HPV-16 and HPV-31 infections were confirmed with real-time E6 polymerase chain reaction. Indeterminate samples were analyzed using Luminex technology. Correlations of the HPV type and histology were examined for statistical significance. RESULTS: The most commonly identified genotype in patients with cervical intraepithelial neoplasia grade 2 or worse was HPV-16 (46.7%) followed by HPV-31 (14.7%) and then HPV-18 (3.9%). Fifteen of 330 specimens that were positive for HPV-16 or HPV-31 were further resolved to be mixed HPV-16/HPV-31 infections by real-time polymerase chain reaction. The risk of cervical intraepithelial neoplasia associated with HPV-18 infection (0.4-1.7) is substantially lower than with either HPV-16 (3.6-11.0) or HPV-31 (1.8-12.6). CONCLUSIONS: HPV-31 is contributing significantly to the proportion of women with cervical intraepithelial neoplasia in our population and shows a higher prevalence than HPV-18 in high-grade lesions. The clinical significance of HPV-31 may be underestimated and its continued significance will depend on the level of cross protection offered by the new vaccines.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".