Human papillomavirus genotype groups and cofactors in the progression of cervical precancerous lesions
Bibliographic record
Abstract
Human papillomavirus (HPV) infection is a necessary cause of cervical carcinogenesis, but high-risk HPV infection alone is not sufficient for the development of cervical cancer. Several factors have been proposed as potential cofactors that act in conjunction with HPV to cause precancerous lesions and cancer. This study sought to assess the role of several environmental risk factors in cervical carcinogenesis and describe the association of HPV genotype groups with severity of cervical precancerous lesions and cancer. Using data from the ongoing Biomarkers of Cervical Cancer Risk case-control study conducted in Montréal, Canada, the associations between environmental and genotypic factors and cervical intraepithelial neoplasia (CIN) grades 1, 2, and 3 and squamous cell carcinoma (SCC) were evaluated using multivariate multinomial logistic regression analysis. The cofactors were compared between each grade of the disease continuum to the grade immediately preceding it and to normal cervical cytology. The associations were explored while controlling for HPV and after restricting to high-risk HPV positive women.HPV status was the strongest risk determinant of CIN and SCC. HPV types 16 and 18 were significantly associated with high-grade lesions and SCC within a population of high-risk HPV positive women. Furthermore, being HPV 16 positive was the strongest predictor for disease progression. Smoking and parity were also identified in this study as potentially important secondary risk factors for cervical carcinogenesis.Determining the factors which act in conjunction with HPV will not only strengthen our general understanding of the etiology of cervical cancer, but will aid greatly in defining high-risk groups and in assisting in primary prevention strategies.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".