Why have breast cancer mortality rates declined?
Bibliographic record
Abstract
The recent decline in breast cancer mortality in the USA might be due to prevention or to screening mammography or to improved treatment protocols. We sought to determine which factors are likely to be responsible for the observed decline in breast cancer mortality. We used the Surveillance, Epidemiology and End Results (SEER) database to estimate incidence rates, mortality rates, and survival from breast cancer for white women who were diagnosed with invasive breast cancer from 1975 to 2011. From 1975 to 2010, the mortality of breast cancer declined from 32 per 100,000 per year to 21 per 100,000 per year (34%). At the same time, the incidence increased by 30%, in particular for localized breast cancers (62%) without a commensurate decline in the number of regional breast cancers. From 1975 to 2002, 10-year survival increased by 28% (from 64.9% to 82.8%). The increase in survival was greater for regional cancers (23%), than for localized (10%) or for distant cancers (3%). The decline in breast cancer mortality in the USA from 1975 to 2010 is unlikely to be the result of advances in prevention or screening. The large increase in the incidence of localized cancers without a corresponding decrease in advanced breast cancers suggests a prominent stage shift, due to overdiagnosis. The drop in the mortality rate could be accounted for by an improvement in cancer survival, likely due to increased use of adjuvant chemotherapy over the period.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".