Incidence and risk factors of second primary cancer after the initial primary human papillomavirus related neoplasms
Bibliographic record
Abstract
Comprehensive studies in second primary cancer (SPC) after the initial primary human papillomavirus (HPV)-related cancer still remain warranted. We aimed to analyze the incidence and risk factors of SPC after HPV-related cancer. We identified 86 790 patients diagnosed with initial primary HPV-related cancer between 1973 and 2010 in the SEER database. Standardized incidence ratio (SIR) and cumulative incidence were calculated to assess the risk of SPC after HPV-related cancer. The SIR of SPC after HPV-related cancer was 1.60 (95% confidence interval [CI], 1.55-1.65) for male and 1.25 (95% CI, 1.22-1.28) for female. SIR of second primary HPV-related cancer (7.39 [95% CI, 6.26-8.68] male and 4.35 [95% CI, 4.04-4.67] female) was significantly higher than that of HPV-unrelated cancer (1.54 [95% CI, 1.49-1.60] male and 1.16 [95% CI, 1.13-1.19] female). The 5-year cumulative incidence of SPC was 7.22% (95% CI, 6.89-7.55%) for male and 3.72% (95% CI, 3.58-3.88%) for female. Risk factors for SPC included being married and having initial primary cancer (IPC) diagnosed at earlier stage for both genders, and IPC diagnosed at older age as well as surgery performed for female. Patients diagnosed with HPV-related cancer are more likely to develop another primary cancer, compared with the age-specific reference population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".