Directionality of Genital Human Papillomavirus Infection Transmission Within Heterosexual Couples: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND: Limited evidence indicates greater female-to-male (F-M) transmission of genital infection with human papillomavirus (HPV) relative to male-to-female (M-F). We verified the hypothesis of a differential transmission rate in couple-based studies by conducting a systematic review and meta-analysis. METHODS: We searched MEDLINE, EMBASE, Scopus, and Cochrane Library databases for studies published until December 2019. We calculated pooled estimates of F-M and M-F transmission rates and their rate differences per 100 person-months, with 95% confidence intervals (CI), using a random-effects model. We counted occurrences of directionality preponderance for each HPV type. RESULTS: We identified 7 eligible studies published between 2008 and 2019, providing data for 752 couples. Pooled estimates for F-M and M-F transmission rates were 3.01 (95% CI, 1.19-7.64; I2 = 97%) and 1.60 (95% CI, 0.86-2.98; I2 = 89%), respectively. The overall rate difference was 0.61 (95% CI, -0.27 to 1.49; I2 = 75%). Three studies provided rates by sex and HPV genotype; 2 favored a preponderance of F-M and 1 favored M-F transmission. CONCLUSIONS: There was slight evidence for a differential transmission rate favoring higher F-M than M-F transmission with substantial statistical heterogeneity across studies.
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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 | 0.021 | 0.051 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.047 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".