US cancer deaths prevented due to survival improvements stratified by extent of disease, 2010-2019
Bibliographic record
Abstract
BACKGROUND: Progress against cancer mortality has been driven by primary prevention, early detection, and cancer treatment. We estimated the number of cancer deaths that were avoided due to stage-specific improvements in cancer survival among patients diagnosed in 2010-2019 followed through 2020. METHODS: We used cancer incidence data from 17 Surveillance, Epidemiology, and End Results (SEER) cancer registries during 2004-2019. We estimated the number of cancer deaths prevented due to cancer- and stage-specific survival improvements (based on SEER summary stage) as the observed minus expected number of cancer deaths through 2020. We calculated the expected number of cancer deaths from estimated cumulative incidence models setting the calendar year effect to 2009. RESULTS: During 2010-2019, there were 3 310 270 incident cancers and 966 733 cancer deaths through 2020 in SEER-17. Improvements in stage-specific cancer survival resulted in a 4.7% (95% CI = -5.3% to -4.2%) decline in cancer deaths in females (22,874 fewer deaths) and a 4.4% (95% CI = -4.9% to -3.9%) decline in males (23 198 fewer deaths) in SEER-17 regions, corresponding to approximately 173 900 fewer cancer deaths in the full US population. The largest absolute declines were for lung and liver cancers, whereas the largest relative declines were observed for melanoma and leukemia. Cancer deaths prevented were not statistically significant for colorectal or prostate cancers. All statistical tests were 2-sided. CONCLUSIONS: Stage-specific survival gains, reflecting treatment advances and improved access to cancer treatment from 2010 to 2019, resulted in an estimated 173 900 fewer cancer deaths among US cancer patients diagnosed during this time 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".