Human Papillomavirus Infections with Multiple Types and Risk of Cervical Neoplasia
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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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 | 0.003 |
| 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 | 0.000 |
| 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".