Predicting the Change in Breast Cancer Deaths in Spain by 2019
Bibliographic record
Abstract
BACKGROUND: Breast cancer mortality rates have been decreasing in Spain since 1992. Recent changes in demography, breast cancer therapy, and early detection of breast cancer may change this trend. METHODS: Using breast cancer mortality data from years 1990 to 2009, we sought to predict the changes in the burden of breast cancer mortality during the years 2005-2019 through a Bayesian age-period-cohort model. The net change in the number of breast cancer deaths between the periods of 2015-2019 and 2005-2009 was separated into changes in population demographics and changes in the risk of death from breast cancer. RESULTS: During the period 1990-2009, breast cancer mortality rates decreased (age-standardized rates per 100,000 women-years 50.6 in 1990-1994 vs. 41.1 in 2005-2009), whereas the number of breast cancer deaths increased (28,149 in 1990-1994; 29,926 in 2005-2009). There was a decrease in the number of cases among women 45-64 years of age (10,942 in 1990-1994; 8,647 in 2005-2009). Changes in population demographics contribute to a total increase of 12.5-12.8% comparing periods 2005-2009 versus 2015-2019, whereas changes in the risk of death from breast cancer contribute to a reduction of 12.9-13.7%. We predict a net decline of 0.1-1.2% in the absolute number of breast cancer deaths comparing these time periods. CONCLUSIONS: The decrease in the risk of death from breast cancer may exceed the projected increase in deaths from growing population size and aging in Spain. These changes may also explain the decrease in the absolute number of breast cancer deaths in Spain since 2005.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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 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".