Premature mortality due to breast cancer among Canadian women: an analysis of a 30-year period from 1980 through 2010
Bibliographic record
Abstract
Background: Breast cancer is the most commonly diagnosed cancer and the second most common cause of cancer deaths for women. In the present study, we examined the trend of premature mortality due to breast cancer among Canadian women from 1980 through 2010 and proposed a new measure of lifespan shortening. Methods: Mortality data for female breast cancer was obtained from the World Health Organization mortality database. Years of life lost (YLL) was estimated using Canadian life tables. Average lifespan shortened (ALSS) that is calculated and expressed by a ratio of YLL relative to expected lifespan. Results: Over this study period, age-standardized rates of breast cancer mortality adjusted to World Standard Population decreased by 40% from 23.2 to 14.2 per 100 000 women. The adjusted YLL rates fell from 5.3 years per 1000 women to 3.3 years. On average women with breast cancer died 20.8 years prior to expected death in 1980 and 18.3 years early in 2010. A novel measure of lifespan shortening, the ALSS decreased from one-fourth of the lifespan in 1980 to one-fifth in 2010. Conclusions: Our study reports that among Canadian women with breast cancer, a smaller proportion of life was lost on average at the end of the study period. The 'life lost' measures presented in this study would be useful tools to monitor the pattern of premature mortality for chronic conditions. These measures gauge the effectiveness of the health system with respect to early detection and treatment.
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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 | 0.008 |
| Science and technology studies | 0.002 | 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".