Second malignancies among survivors of germ‐cell testicular cancer: A pooled analysis between 13 cancer registries
Bibliographic record
Abstract
We investigated the risk of second malignancies among 29,511 survivors of germ-cell testicular cancer recorded in 13 cancer registries. Standardized incidence ratios (SIRs) were estimated comparing the observed numbers of second malignancies with the expected numbers obtained from sex-, age-, period- and population-specific incidence rates. Seminomas and nonseminomas, the 2 main histological groups of testicular cancer, were analyzed separately. During a median follow-up period of 8.3 years (0-35 years), we observed 1,811 second tumors, with a corresponding SIR of 1.65 (95% confidence interval (CI): 1.57-1.73). Statistically significant increased risks were found for fifteen cancer types, including SIRs of 2.0 or higher for cancers of the stomach, gallbladder and bile ducts, pancreas, bladder, kidney, thyroid, and for soft-tissue sarcoma, nonmelanoma skin cancer and myeloid leukemia. The SIR for myeloid leukemia was 2.39 (95% CI: 1.41-3.77) after seminomas, and 6.77 (95% CI: 4.14-10.5) after nonseminomas. It increased to 37.9 (95% CI: 18.9-67.8; based on 11 observed cases of leukemia) among nonseminoma patients diagnosed since 1990. SIRs for most solid cancers increased with follow-up duration, whereas they did not change with year of testicular cancer diagnosis. Among subjects diagnosed before 1980, 20 year survivors of seminoma had a cumulative risk of solid cancer of 9.6% (95% CI: 8.7-10.5%) vs. 6.5% expected, whereas 20 years survivors of nonseminoma had a risk of 5.0% (95% CI: 4.2-6.0%) vs. 3.1% expected. In conclusion, survivors of testicular cancers have an increased risk of several second primaries, where the effect of the treatment seems to play a major role.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".