Epidemiological trends of head and neck Cancer survivors in Alberta: Towards improved understanding of the burden of disease
Bibliographic record
Abstract
BACKGROUND: With an increase in the incidence of human papillomavirus (HPV)-associated oropharyngeal squamous cell carcinoma (OPSCC) and more favourable survival outcomes, there is now a population of head and neck cancer survivors that are different from preceding decades. In addition, their long-term survivorship issues have become increasing research interests. This study was undertaken to determine the changing epidemiological trends of head and neck cancer survivors in Alberta to better anticipate future demands on healthcare services. METHODS: The Alberta Cancer Registry was queried for adult (aged > 18 years), head and neck cancer (HNC) patients who were at least 1-year post-treatment completion between 1997 to 2016. Cutaneous head and neck and thyroid cancer patients were excluded. Extracted data was then used to calculate the incidence and prevalence of early (< 5 years from treatment), intermediate (5 to < 10 years from treatment), and late (> 10 years from treatment) survivors of head and neck cancer. Point prevalence of HNC survivors in 2005, 2010, and 2015 were then further stratified by gender, sub-site and age. RESULTS: Over this time period, head and neck cancer survivors tended to be younger (64.0 vs. 62.1, p = 0.046) and male (M:F 2.45:1 vs 2.54:1). In 1997, the predominant subsites were the oral cavity and larynx at 45.8% and 30.9%, respectively. In 2015 the predominant subsites were the oral cavity and oropharynx at 33.0% and 29.4%, respectively. Within the cohort, the prevalence of late HNC survivors increased to 13.3 per 100,000 people in 2015. CONCLUSIONS: There is a significant population of head and neck survivors who are younger, male, and more than 10 years post-treatment. While oral cavity cancers have shown stable disease prevalence in recent decades, the number of OPSCC survivors have increased. With an improved understanding of the distribution and characteristics of HNC survivors, a more guided healthcare support network can be fostered for these patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".