Associations between first and second primary cancers: a population-based study
Bibliographic record
Abstract
BACKGROUND: Patients surviving certain types of cancer are at increased risk of a second primary cancer. We tested the hypothesis that excess risk of a second primary cancer is due mainly to excess risk of it being the same type of cancer as the first, rather than to excess risk of it being a different type. METHODS: We conducted a nationwide study using data from three dabatases for the entire Danish population (n = 7,493,705) from 1980 through 2007. For each type of cancer, we performed a nested study matching each patient with incident cancer diagnosed in that period with up to five controls who did not have the examined cancer at the time of diagnosis. We used Cox regression models to calculate individual risk estimates and meta-analysis techniques to calculate aggregated risk estimates. RESULTS: A total of 765,255 people had one or more diagnoses of primary cancer (total 843,118 diagnoses) during the study period. The aggregated hazard ratio (HR) for risk of any second primary cancer after any first cancer was 1.25 (95% confidence interval [CI] 1.24-1.26), with heterogeneity among cancer types. The aggregated HR for risk of a second primary cancer of the same type as the first was 2.16 (95% CI 1.98-2.34). The aggregated HR for risk of a second cancer of a different type from the first was 1.13 (95% CI 1.12-1.15). Results were similar when we excluded second primary cancers occurring within 1, 2, 5 or 10 years after the first cancer. Overall, we observed 74 significant associations among 27 types of first cancer and 27 possible types of second primary cancer. INTERPRETATION: Excess risk of a second primary cancer was due mainly to a 2.2-fold risk of the second cancer being the same type as the first, whereas the risk of it being a different type was only 1.1-fold. However, heterogeneity among cancer types was substantial.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".