Effect of Mammography Screening on Incidence and Mortality of Breast Cancer in Alberta
Bibliographic record
Abstract
Background: Breast cancer is the second leading cause of cancer death among Canadian women, and to decrease this burden mammography screening is widespread. If effective, mammography screening should reduce the incidence of late-stage cancer by early detection, allow time for prompt treatment and result in lower mortality. Given Alberta’s universal health system, with organised screening reaching around 63% of the target population annually, we set out to determine how much screening mammography has decreased presentation of late-stage cancer, and potentially reduced mortality from breast cancer, among Alberta women. Methods: We conducted a historical birth-cohort study and trend analysis using data from the Alberta Cancer registry from 1982 to 2017. We compared stage specific incidence and mortality over the years and by birth cohorts, taking into consideration the introduction and evolution of screening mammography to measure how much effect screening has on observed trends. We used Joinpoint regression analysis to test statistically significance of observed trends. Results: From 2006 to 2017, incidence of early-stage breast cancers among women aged 50 to 79 years increased by 33 per 100,000 women at an average rate of 1.2% annually (p<0.001), while incidence of late-stage cancer decreased by 3 per 100,000 women at a rate of 0.8 annually (p=0.3). From 2001 to 2018, deaths from breast cancer reduced by 29 per 100,000 women at 2.3% annually (p<0.001), while all-cause mortality reduced by 9 per 100,000 at 0.5% annually (p=0.1) in women previously diagnosed with breast cancer. Each subsequent recent birth cohort had higher rates of early breast cancer at specific ages while the incidence of late-stage cancers reduced with recent cohorts at specific ages. Conclusion: There has been some reduction in the incidence of late-stage breast cancer and breast cancer deaths between 2006 and 2018. This has been associated with an excess increase in early-stage cancers, which may be explained by overdiagnosis. These may be related to changes in screening mammography in that period. Women need to be educated on the effectiveness of screening mammography in order to make informed decisions about their screening practices
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".