Bibliographic record
Abstract
No sponsorships or competing interests have been disclosed for this article. Objectives. The objective of this study was to investigate the changes in the epidemiology (incidence, age at diagnosis, and survival) of head and neck cancers (HNCs) in Canada in the past decade. Study Design. Analysis of a national cancer data registry. Setting. All Canadian hospital institutions treating head and neck cancer. Subjects and Methods. Using Canadian Cancer Registry data (1992-2007), the authors categorized HNCs into 3 groups according to their possible association with human papillo-mavirus (HPV): oropharynx (highly associated), oral cavity (moderate association), and ‘‘other’ ’ (hypopharynx, larynx, and nasopharynx), which are not HPV related. They calcu-lated age-adjusted incidence, median age at diagnosis, and survival for each category. Results. Oropharynx tumors increased in incidence over the study time period (annual percent change: 1.50 % men, 0.8% women), whereas oral cavity tumors decreased (2.10 % men, 0.4 % women), as did other HNCs (decreased by 3.0 % for men and 1.9 % for women). The median age at diagnosis for oro-pharynx cancer decreased by an average of 0.23 years/y. There was no change for oral cavity tumors but an increase for other HNCs of 0.12 years/y. Survival for patients with oropharynx cancer increased by 1.5%/y but was significant for men only. Survival for patients with oral cavity and other HNCs also increased in men only by 0.9%/y and 0.25%/y, respectively. Conclusion. Oropharynx cancer, which is highly correlated with HPV infection, is increasing in incidence in Canada, with a decreasing age at diagnosis and an improvement in survival. This could have implications for screening strategies and treatment for oropharyngeal cancers in Canada. Keywords head and neck cancer, oropharynx cancer, human papilloma-
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.906 | 0.815 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".