Cervical Infection with Cutaneous Beta and Mucosal Alpha Papillomaviruses
Bibliographic record
Abstract
Abstract Background: Alpha-human papillomavirus (α-HPV) plays a causal role in cervical cancer, but little is known about the epidemiology of genital Beta-human papillomavirus (β-HPV) infection. Methods: We used Luminex and PCR hybridization to detect β- and α-HPVs prevalence at enrollment and 12-month follow-up in cervical samples from 505 women enrolled in the Ludwig-McGill cohort study. We compared epidemiologic correlates of both β- and α-HPVs and compared genotypes between these genera with respect to co-occurrence and association with cervical cytologic abnormalities. Results: Infection with β-HPV types was more prevalent than that with α-HPV types at both visits (cumulative prevalences: 27.3% vs. 21.6%, respectively, P = 0.034). β-HPVs were mostly transient; however, only 1.98% women retained their original positivity at 12 months, whereas persistence was higher for α-HPVs (5.15%; P = 0.007). Age, parity, and sexual activity variables were predictors of α-HPV but not of β-HPV. α- and β-HPV types occurred independently. Increased risk of cervical abnormalities was restricted to women infected with α-9 or α-6 HPV types. We found no epidemiologic correlates for β-HPV infections. Conclusions: Detection of β-HPV types in the cervix tends to occur as random and transient episodes not explained via the sexual-transmission correlates that characterize infections by α-HPVs. Impact: Although it is plausible that β-HPVs may play a direct or indirect carcinogenic role, the lack of epidemiologic correlates for detection episodes of these viruses and lack of association with cervical lesions speak against their ancillary role as sexually transmitted agents in cervical carcinogenesis. Cancer Epidemiol Biomarkers Prev; 26(8); 1312–20. ©2017 AACR.
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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 | 0.000 | 0.001 |
| 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) | 0.005 | 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".