Trends in Diagnosis and Treatment of Metastatic Cancer in the United States
Bibliographic record
Abstract
OBJECTIVES: Metastatic cancer has historically been considered fatal; however, there is a paucity of evidence characterizing the epidemiology of incidence, treatment, and outcomes in these patients. MATERIALS AND METHODS: Incidence rates, annual percent change (APC), descriptive epidemiological statistics, and odds ratios for survival were calculated using registry data from the Surveillance, Epidemiology, and End Results (SEER) and the National Cancer Database (NCDB), 1998 to 2015. RESULTS: There were a total of 1,055,860 patients with metastatic cancer. The most frequent primary cancers were lung (42.6%), colorectal (9.5%), and ovarian (5.5%). Metastatic lung and colorectal cancer incidence decreased, APC: -1.57 (P<0.001) and APC: -1.48 (P<0.001), respectively; metastatic pancreatic cancer incidence increased, APC: 0.62 (P=0.001). The use of local therapies decreased for almost all sites, and the use of systemic therapies increased across multiple sites: single-agent chemotherapy in kidney (2.54% increase/year), female breast (1.14% increase/year), and prostate cancer (1.08% increase/year); multiagent chemotherapy, most notably in pancreas (2.23% increase/year), uterus (1.81% increase/year), and colorectal cancer (1.54% increase/year). Increased utilization of immunotherapy was observed across the majority of sites, most notably in melanoma (2.14% increase/year). Patients diagnosed from 2006 to 2010 had 17.4% higher odds of surviving at least 60 months compared with 1998 to 2002. CONCLUSIONS: In this study, metastatic disease has been shown to have unique epidemiological patterns, and survival has improved. Continued research on metastatic disease is important in understanding and addressing the distinct health concerns of this population.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".