Prevalence of Breast Cancer Survivors Among Canadian Women
Bibliographic record
Abstract
BACKGROUND: The 49% decrease in breast cancer mortality since 1986 has increased the number of breast cancer survivors requiring survivorship care. The purpose of this analysis was to estimate the 2022 prevalence of breast cancer survivors diagnosed within the past 15 years among Canadian women. METHODS: We extracted the projected female breast cancer cases from 2007 to 2021 and rates of net survival (competing noncancer causes of death removed) from the Canadian Cancer Society's statistical reports. Overall survival was extracted from published Ontario data. Using known survival rates for 1, 5, 10, and 15 years, we interpolated remaining years and applied the corresponding net and overall survival rates to the projected cases for each year from 2007 to 2021 to determine survivors in 2022. Prevalence for predefined age groups was also calculated. As an example of excess healthcare costs attributable to breast cancer, we calculated the excess costs of heart failure hospitalizations. RESULTS: From 2007 to 2021, there were 370,756 breast cancer cases. Using net survival, 318,429 (85.9%) of these patients were projected to survive breast cancer by 2022, a prevalence of 2.1% of Canadian women. Using overall survival, prevalence was 1.8%. Prevalence increased with age group, from 0.01% of those aged 20 to 24 years to 12.7% of those aged ≥90 years, and from 1.0% among the working and/or child-raising (age 20-64 years) to 5.4% among elderly populations (age ≥65 years). Among these survivors, 24.9% of projected heart failure hospitalizations would be in excess of those among matched control subjects, with projected excess costs of $16.5 million CAD. Given the excess healthcare costs, potential for reduced contributions to the workforce, and reduced quality of life associated with long-term impairments and risk of excess non-breast cancer death, enhanced breast cancer survivorship care is warranted. CONCLUSIONS: With an overall prevalence of 2% among Canadian women, breast cancer survivors represent an increasing segment of the working-age and elderly populations.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".