Proportion of Incident Genital Human Papillomavirus Detections not Attributable to Transmission and Potentially Attributable to Latent Infections: Implications for Cervical Cancer Screening
Bibliographic record
Abstract
BACKGROUND: Infections with human papillomaviruses (HPVs) may enter a latent state, and eventually become reactivated following loss of immune control. It is unclear what proportion of incident HPV detections are reactivations of previous latent infections vs new transmissions. METHODS: The HPV Infection and Transmission among Couples through Heterosexual activity (HITCH) cohort study prospectively followed young newly formed heterosexual partners recruited between 2005 and 2011 in Montréal, Canada. We calculated the fraction of incident HPV detections nonattributable to sexual transmission risk factors with a Bayesian Markov model. Results are the median (2.5th-97.5th percentiles) of the estimated posterior distribution. RESULTS: A total of 544 type-specific incident HPV detection events occurred in 849 participants; 33% of incident HPV detections occurred in participants whose HITCH partners were negative for that HPV type and who reported no other sex partners over follow-up. We estimate that 43% (38%-48%) of all incident HPV detections in this population were not attributable to recent sexual transmission and might be potentially reactivation of latent infections. CONCLUSIONS: A positive HPV test result in many cases may be a reactivated past infection, rather than a new infection from recent sexual behaviors or partner infidelity. The potential for reactivation of latent infections in previously HPV-negative women should be considered in the context of cervical cancer screening.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".