Human Papillomavirus Infections with Multiple Types and Risk of Cervical Neoplasia
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Bibliographic record
Abstract
BACKGROUND: Besides an established role for certain human papillomavirus (HPV) genotypes in the etiology of cervical cancer, little is known about the influence of multiple-type HPV infections on cervical lesion risk. We studied the association between multiple HPV types and cervical lesions among 2,462 Brazilian women participating in the Ludwig-McGill study group investigation of the natural history of HPVs and cervical neoplasia. METHODS: Cervical specimens were typed by a PCR protocol. The cohort's repeated-measurement design permitted the assessment of the relation between the cumulative and concurrent number of HPV types and any-grade squamous intraepithelial lesions (SIL) and high-grade SIL (HSIL). RESULT: At individual visits, 1.9% to 3.2% of the women were infected with multiple HPVs. Cumulatively during the first year and the first 4 years of follow-up, 12.3% and 22.3% were infected with multiple types, respectively. HSIL risk markedly increased with the number of types [odds ratio (OR), 41.5; 95% confidence interval (95% CI), 5.3-323.2 for single-type infections; OR, 91.7; 95% CI, 11.6-728.1 for two to three types; and OR, 424.0; 95% CI, 31.8-5651.8 for four to six types, relative to women consistently HPV-negative during the first year of follow-up]. The excess risks for multiple-type infections remained after exclusion of women infected with HPV-16, with high-risk HPV types, or persistent infections, particularly for any-grade SIL. Coinfections involving HPV-16 and HPV-58 seemed particularly prone to increase risk. CONCLUSION: Infections with multiple HPV types seem to act synergistically in cervical carcinogenesis. These findings have implications for the management of cervical lesions and prediction of the outcome of HPV infections.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it