Relative survival analysis using the Centers for Disease Control and Prevention's National Program of Cancer Registries Surveillance System Data, 2000-2007.
Bibliographic record
Abstract
BACKGROUND: Cancer survival rates are important in evaluating cancer care, identifying disease patterns, and estimating the probability of death due to cancer. To date, survival rates have been calculated using other data sets with limited population coverage that may not be able to fully identify differences by treatment, geographic region, and racial or ethnic group. Data from the Centers for Disease Control and Prevention's (CDC's) National Program of Cancer Registries (NPCR) have not been used previously to calculate relative survival rates within the United States. METHODS: Data from CDC's November 2011 submission for 21 state population-based central cancer registries, representing 50 percent of the US population, were included in this analysis. This paper presents relative survival rates for diagnosis years 2000-2007 with follow-up through 2008. RESULTS: The relative survival rate for all cancers and races combined was 65.0 percent (65.3 percent for male and 64.8 percent for female patients). Black patients had a lower relative survival rate than white patients, except for lung and bronchus. For all cancers, the under 45 age groups had the highest relative survival rates, except for black males. DISCUSSION: For all cancer primary sites combined for 2000-2007, the CDC NPCR 5-year relative survival rate is comparable to that reported by the National Cancer Institute and the Canadian Cancer Registry. This analysis presents, for the first time, relative survival rates for half of the total US population and demonstrates that reliable survival rates can be calculated using CDC's NPCR data now and in the future.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| 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".