Premature deaths due to head and neck cancers in canada: A nationwide analysis from 1980 to 2010
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: To examine if there has been a reduction in premature mortality due to head and neck cancers and their subtypes in the Canadian population between 1980 and 2010. STUDY DESIGN: Retrospective database review. METHODS: Mortality data for head and neck cancers were obtained from the World Health Organization's mortality database. Years of life lost (YLL) was calculated according to Canadian life tables. A new measure, average lifespan shortened (ALSS), was calculated as a ratio of YLL over the expected lifespan. RESULTS: During the period of interest, decreases in age standardized rates of death adjusted to the World Standard Population were observed mainly among men. The adjusted YLL rates remained unchanged with respect to oral cancers in women, but slightly decreased for oral cancers in men and for laryngeal cancers in both sexes. Our new measure of ALSS showed that in 1980, patients with oral cancers lost an average of 20% of their lifespan, whereas in 2010, men and women still lost approximately 20% and 18%, respectively. Patients with laryngeal cancers lost 18% of their lifespan in both sexes in 1980 and 17% in 2010. The ALSS subanalysis showed that the largest gain of lifespan was observed for cancers of the hypopharynx in men and for cancers of the floor of mouth and nasopharynx in women. CONCLUSIONS: There appears to be modest improvements in premature mortality due to head and neck cancers in Canada over a 30-year period, with the exception of oral cancers in men. Changes in premature mortality varied slightly by cancer subtypes. LEVEL OF EVIDENCE: NA Laryngoscope, 130:911-917, 2020.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".