Skin cancer risk among testicular germ‐cell cancer survivors: a systematic review and meta‐analysis
Bibliographic record
Abstract
Testicular germ-cell tumours (TGCT) are the most common cancer among young adult men. Previous studies suggested TGCT survivors have an increased risk for skin cancer. The goal of this study was to systematically review the literature and evidence regarding skin cancer risk among TGCT survivors compared with the general population. PubMed, EMBASE, Web of Science, Cochrane Databases and reference lists were included in the search. A systematic review of all comparative studies with more than 10 TGCT survivors reporting on skin cancer incidence was performed. A meta-analysis of the Standardized Incidence Rate (SIR) was calculated by pooling study-specific log-transformed estimates using the random-effects model. Risk of bias was assessed using the Newcastle-Ottawa Quality Assessment Scale. Nineteen studies that reported on 147 935 TGCT survivors were included. Pooled SIR for skin cancer and for melanoma incidence among TGCT survivors were 1.93 (95% CI 1.62-2.29, P < 0.0001) and 1.81 (95% CI 1.57-2.08, P < 0.0001), respectively. In conclusion, compared to the general population, TGCT survivors have an increased risk for developing skin cancer and melanoma. Additional long-term studies that include TGCT survivors, additional risk factors and all subtypes of skin cancer are required.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.019 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".