Secondary malignancies after treatment of testicular germ cell tumors: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Testicular germ cell tumors (TGCTs) are the most common malignancy in men 15-35 years of age. Management options for men with TGCTs include surgery, radiation, and/or chemotherapy. Given TGCTs' excellent survival, most patients live long enough to experience delayed treatment toxicities, warranting careful consideration of therapeutic decisions. An important outcome of interest is the development of secondary malignant neoplasms (SMNs). METHODS: A systematic literature search was conducted through a combination of database searches (Medline, EMBASE, and Cochrane library) and manual review. Studies evaluating the incidence of SMNs in patients following treatment for TGCTs were identified. Our primary outcome was the diagnosis of any non-germ cell SMN following treatment, compared with the general population. Meta-analyses were performed using random-effects models, with outcomes reported as standardized incidence ratios (SIRs). Strength of evidence was evaluated using the GRADE framework. RESULTS: Twenty-one studies including 88 863 patients with 5180 SMNs were included. Median follow-up was 12.5 years. The incidence of non-germ cell SMNs following definitive treatment of TGCTs varied by treatment modality. Surgery alone was not associated with an increased risk (SIR = 0.99, 95% confidence interval [CI] = 0.84 to 1.17); radiation (SIR = 1.66, 95% CI = 1.43 to 1.93), chemotherapy (SIR = 1.65, 95% CI = 1.39 to 1.96), and combined chemotherapy and radiation (SIR = 2.73, 95% CI = 2.23 to 3.33) were associated with a moderate to large increase in risk. There was low to moderate certainty in quality of evidence by GRADE framework. CONCLUSIONS: Chemotherapy, radiation, and their combination are associated with an increased risk of non-germ cell SMNs after the treatment of TGCTs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.034 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".