Lack of Association between Human Papillomavirus Types 6 and 11 Genetic Variants and Cervical Abnormalities: The Ludwig–McGill Cohort Study
Bibliographic record
Abstract
BACKGROUND: Human papillomavirus (HPV) types 6 and 11 are mainly associated with the development of genital warts and recurrent respiratory papillomatosis. We examined intratypic genetic variability of both viral types with the development of cervical cytologic abnormalities in Brazilian women. METHODS: We used PCR sequencing to characterize variants of HPVs 6 and/or 11 in cervical swabs from women in the Ludwig-McGill Cohort Study. We used a binomial generalized estimating equations (GEE) model with logit link to estimate odds ratios (OR) and 95% confidence intervals (CI) for the associations between HPV 6 and 11 variants and cytologic abnormalities. RESULTS: B1 and B3 HPV6 and A2 HPV11 variants were the most common isolates identified. Compared with HPV6-negative women, the ORs among women harboring HPV6 B1 or B3 variants were 6.3 (95% CI, 2.3-17.0) and 2.3 (95% CI, 0.6-9.7) for atypical cells of undetermined significance (ASCUS)/low squamous intraepithelial lesions (LSIL), respectively, and 1.7 (95% CI, 0.6-5.1) and 1.2 (95% CI, 0.3-4.7) for ASCUS/LSIL/high squamous intraepithelial lesions (HSIL). Respective ORs were 5.0 (95% CI, 1.7-14.6) and 2.8 (95% CI, 1.0-8.1) upon comparing women with HPV11 A2 variants to HPV11-negative women. All associations disappeared when adjusting for coinfections with high-risk HPV types. CONCLUSIONS: Our data do not support an association between low-risk HPVs 6 and 11 genetic variability and cervical abnormalities. IMPACT: Risk of cervical cytologic abnormalities is not affected by intratypic polymorphism in HPVs 6 and 11.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".