Prevalence and genotype distribution of human papillomavirus in cervical adenocarcinoma (usual type and variants): A systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract Cervical glandular neoplasms represent a heterogeneous group of tumors for which a comprehensive overview of the involvement of high‐risk human papillomaviruses (HPV) in pathogenesis is still lacking. We first searched MEDLINE (PubMed), Embase, and Scopus databases (until October 2022), and systematically reviewed available literature. We then quantitatively estimated both pooled and genotype‐specific prevalence of HPV DNA as well as the influence of various factors (e.g., geographical region, histological subtype, tissue/sample type) on computed effect size by means of random effects meta‐analysis. In total, 379 studies comprising 17 129 cases of cervical adenocarcinoma were identified. The pooled HPV prevalence was 78.4% (95% confidence interval [95% CI]: 76.2–80.3) with a significant between‐study heterogeneity ( I 2 = 79.4%, Q test p < 0.0001). Subgroup analyses indicated that the effect size differed substantially by geographical region (from 72.5% [95% CI: 68.7–76.1] in Asia to 86.8% [95% CI: 82.2–90.3] in Oceania) ( p < 0.0001) and histological subtype of cancer (from 9.8% [95% CI: 5.5–17] in gastric‐type to 85% [95% CI: 79.6–89.2] in usual‐type cervical adenocarcinoma) ( p < 0.0001). HPV16 and HPV18 were by far the most frequently detected viral strains with specific prevalence of 49.8% (95% CI: 46.9–52.6) and 45.3% (95% CI: 42.8–47.8), respectively. When stratified by continent or histologic variant, these genotype‐specific results varied in a relatively limited manner. Altogether, these findings support that all histological subtypes of cervical adenocarcinoma are etiologically linked to high‐risk HPV but to varying degrees. Therefore, a dual‐criteria classification taking into account accurately both morphological and virological aspects could be an interesting evolution of the current binary World Health Organization classification, better reflecting the pathogenic diversity of the disease.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".