Bibliographic record
Abstract
According to world-wide estimates, human papil-lomavirus (HPV) infection is responsible for 5.2%of all cancers.1,2 The association between HPV and cervical cancer has been well-known for many decades, but the link between HPV and cancers of the anus, penis, vagina, vulva and oropharynx has only recently been con-firmed.3 Cervical cancer rates have been declining in Canada and the United States, consistent with the success-ful adoption of Papanicolaou smear screening programs,4,5 but the incidence of other HPV-related cancers has been increasing. The increasing incidence of these HPV-related cancers has been attributed to changes in lifestyle-related risk factors, most notably sexual behaviour.5 Analysis of trends in incidence and birth cohorts in both the US and the United Kingdom have shown an increased risk of HPV-related cancer in more recent birth cohorts and recent time periods,5,6 and these increases have been attrib-uted to generational changes in sexual patterns and increased exposure to HPV.6 HPV vaccines are now broadly used in the prevention of cervical cancer. In Alberta, HPV vaccination programs were implemented in 2008 and, as in many other jurisdictions, have been funded only for females. Although HPV vaccination programs may reduce the incidence of HPV-related cancers other than cervical cancer,6 they remain the subject of much public policy debate in Canada.7 In this study, we sought to examine trends in noncervical HPV-related cancers over time and compare them with relative changes in cervical cancer and non–HPV-related cancers with similar risk factors. Trends in the incidence of human papillomavirus-related noncervical and cervical cancers in Alberta, Canada: a population-based study
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.732 | 0.454 |
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".