Increased stomach cancer risk following radiotherapy for testicular cancer
Bibliographic record
Abstract
BACKGROUND: Abdominal radiotherapy for testicular cancer (TC) increases risk for second stomach cancer, although data on the radiation dose-response relationship are sparse. METHODS: In a cohort of 22,269 5-year TC survivors diagnosed during 1959-1987, doses to stomach subsites were estimated for 92 patients who developed stomach cancer and 180 matched controls. Chemotherapy details were recorded. Odds ratios (ORs) were estimated using logistic regression. RESULTS: Cumulative incidence of second primary stomach cancer was 1.45% at 30 years after TC diagnosis. The TC survivors who received radiotherapy (87 (95%) cases, 151 (84%) controls) had a 5.9-fold (95% confidence interval (CI) 1.7-20.7) increased risk of stomach cancer. Risk increased with increasing stomach dose (P-trend<0.001), with an OR of 20.5 (3.7-114.3) for ⩾50.0 Gy compared with <10 Gy. Radiation-related risks remained elevated ⩾20 years after exposure (P<0.001). Risk after any chemotherapy was not elevated (OR=1.1; 95% CI 0.5-2.5; 14 cases and 23 controls). CONCLUSIONS: Radiotherapy for TC involving parts of the stomach increased gastric cancer risk for several decades, with the highest risks after stomach doses of ⩾30 Gy. Clinicians should be aware of these excesses when previously irradiated TC survivors present with gastrointestinal symptoms and when any radiotherapy is considered in newly diagnosed TC patients.
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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 | 0.000 |
| 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) | 0.002 | 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".