HPV-associated lung cancers: an international pooled analysis
Bibliographic record
Abstract
Human papillomavirus (HPV) is the etiologic risk factor for cervical cancer. Some studies have suggested an association with a subset of lung tumors, but the etiologic link has not been firmly established. We performed an international pooled analysis of cross-sectional studies (27 datasets, n = 3249 patients) to evaluate HPV DNA prevalence in lung cancer and to investigate viral presence according to clinical and demographic characteristics. HPV16/18 were the most commonly detected, but with substantial variation in viral prevalence between geographic regions. The highest prevalence of HPV16/18 was observed in South and Central America, followed by Asia, North America and Europe (adjusted prevalence rates = 22, 5, 4 and 3%, respectively). Higher HPV16 prevalence was noted in each geographic region compared with HPV18, except in North America. HPV16/18-positive lung cancer was less likely observed among White race (adjusted odds ratio [OR] = 0.33, 95% confidence interval [CI] = 0.12-0.90), whereas no associations were observed with gender, smoking history, age, histology or stage. Comparisons between tumor and normal lung tissue show that HPV was more likely to be present in lung cancer rather than normal lung tissues (OR = 3.86, 95% CI = 2.87-5.19). Among a subset of patients with HPV16-positive tumors, integration was primarily among female patients (93%, 13/14), while the physical status in male cases (N = 14) was inconsistent. Our findings confirm that HPV DNA is present in a small fraction of lung tumors, with large geographic variations. Further comprehensive analysis is needed to assess whether this association reflects a causal relationship.
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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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.025 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".