Human Papillomavirus-Associated Subsequent Malignancies among Long-Term Survivors of Pediatric and Young Adult Cancers
Bibliographic record
Abstract
Long-term survivors of pediatric and young adult (PAYA) cancers have a high incidence of subsequent neoplasms, but few risk factors other than cancer treatment have been identified. We aimed to describe the burden of human papillomavirus (HPV)-associated malignancies among survivors of PAYA cancers to assess whether HPV infections might be a reasonable area of future etiologic research on subsequent malignancies in this population. We used longitudinal data from 9 population-based registries of the Surveillance, Epidemiology, and End Results program collected between 1973 and 2010 to assemble a cohort of individuals who were diagnosed with any cancer between the ages of 0 and 29 years and survived at least 5 years post-diagnosis. We estimated sex-specific standardized incidence ratios (SIRs) with corresponding 95% confidence limits (CL) of HPV-associated subsequent malignancies (cervical, vaginal, vulvar, penile, anal, tongue, tonsillar, and oropharyngeal). Our study population comprised 64,547 long-term survivors of PAYA cancers diagnosed between 1973 and 2010. Compared with females in the general US population, female PAYA cancer survivors had a 40% relative excess of HPV-associated malignancies overall (SIR = 1.4, 95% CL: 1.2, 1.8). Compared with males in the general US population, male PAYA cancer survivors had a 150% relative excess of HPV-associated malignancies overall (SIR = 2.5, 95% CL: 1.9, 3.4). Our findings suggest an excess of HPV-associated malignancies among PAYA cancer survivors compared with the general US population. We hypothesize that a portion of subsequent malignancies among PAYA cancer survivors may be directly attributable to HPV infection. This hypothesis warrants exploration in future 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 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".