Incidence and Mortality Rates of Second Pancreatic Cancer Among Survivors of Digestive Cancers
Bibliographic record
Abstract
OBJECTIVES: We analyzed the incidence and mortality rates of second pancreatic ductal adenocarcinoma (PDAC) among survivors of digestive cancers in South Korea. METHODS: We evaluated data from the Korea National Health Insurance to identify individuals with digestive cancers in 2005 to 2015. The standardized incidence ratios (SIRs) of second PDACs and survival rates were evaluated. RESULTS: Among 772,534 patients with first digestive cancers, 1696 (0.22%) developed second PDACs. The incidence of second PDACs increased until 10 years since the first cancer diagnosis. Patients with biliary tract cancers (BTCs) showed a higher incidence of second PDACs than did those with gastrointestinal cancers or hepatocellular carcinoma. In ages 20 to 49 years, SIRs (95% confidence interval) were higher in survivors of hepatocellular carcinoma (3.08; 1.04-3.08), gastric cancer (3.40; 1.90-3.40), colorectal cancer (5.00; 2.75-5.00), gallbladder cancer (58.52; 11.81-58.52), intrahepatic cholangiocarcinoma (86.99; 1.73-86.99), extrahepatic cholangiocarcinoma (89.41; 27.42-89.41), and ampulla of Vater cancer (156.78; 48.08-156.78). In ages 50 to 64 years, colorectal cancer (1.42; 1.04-1.42), gastric cancer (1.66; 1.29-1.66), and BTCs revealed higher SIRs. In ages more than 65 years, SIR was increased only in BTCs. Second PDACs revealed a more favorable prognosis than first PDACs. CONCLUSIONS: Careful surveillance for second PDACs after curative treatment of BTCs and colorectal cancers should be considered.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".