HPV and Head and Neck Cancer in Canada: Trends 1992 to 2008
Bibliographic record
Abstract
Objective 1) Learn how the incidence of HPV‐related and non‐HPV‐related Head and Neck Cancers (HNC) in Canada has changed in the time period 1992 to 2008. 2) Learn how the age at diagnosis and overall survival for these cancers in Canada has changed over that period. Method We used Canadian Cancer Registry Data (1992‐2008), categorizing HNCs into 3 groups: (High (HHPV), ie, oropharynx; Moderate (MHPV), ie, oral cavity; and Low (LHPV), ie, larynx); based on the probability that HPV causes the cancer. We calculated age‐adjusted incidence, median age at diagnosis, and survival for each category. Results HHPV cancers increased in incidence at an average annual rate (AAR) of 1.02% (P =. 010); MHPV and LHPV cancers decreased at an AAR of 2.38% (P =. 000) and 3.67% (P =. 000) respectively. The median age at diagnosis for HHPV cancers decreased by an average of 0.23 years/year (P =. 000). There was no change for MHPV and an increase for LHPV of 0.10 years/year (P =. 008). Survival for patients with HHPV cancers increased by 2.1%/year (P =. 000), compared with an increase of 1.6% per year for MHPV (P =. 003) and a marginal increase in LHPV of 0.6% per year (P =. 002). Conclusion The prevalence of HPV‐related head and neck cancers in Canada is increasing, while the prevalence of non‐HPV–related head and neck cancers is decreasing. This has been accompanied by a decrease in both age at diagnosis and mortality in HPV related head and neck cancers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".