Association of Diagnostic Radiation Exposure and Second Abdominal-Pelvic Malignancies After Testicular Cancer
Bibliographic record
Abstract
PURPOSE: The evidence associating cancer risk with diagnostic radiation exposure is unclear. Men recovering from low-grade testicular cancer frequently undergo serial abdominal-pelvic computerized tomography (CT) scanning to monitor for recurrent disease. METHODS: We used population-based administrative data sets to identify every incident case of testicular cancer between 1991 and 2004 in Ontario, Canada. We excluded those with previous cancer, concurrent radiation therapy, retroperitoneal lymph node dissection, or fewer than 5 years observation. Patients were observed until the occurrences of death or development of a second abdominal-pelvic malignancy or until December 31, 2009. RESULTS: A total of 2,569 men (mean age, 34.7 years; standard deviation, 10.2) were observed for a median of 11.2 years (interquartile range [IQR], 8.3 to 14.3). During the first 5 years after diagnosis, men underwent a median of 10 computed tomography (CT) scans (IQR, 4 to 18) of the abdominal-pelvic area, and they were exposed to a median of 110 mSv of radiation from radiologic investigations (IQR, 44 to 190). After this, 14 men were diagnosed with a second abdominal-pelvic malignancy (rate, five per 10,000 patient-years observation, 95% CI, three to eight); the most common diagnoses were colorectal and kidney malignancies. Radiation exposure was not associated with an excess risk of second cancers (hazard ratio per 10 mSv increase, 0.99; 95% CI, 0.95 to 1.04). This association did not change if men observed for fewer than 5 years were included in the analysis (hazard ratio, 1.00; 95% CI, 0.96 to 1.04). CONCLUSION: Second malignancies of the abdomen-pelvis are uncommon in men with low-grade testicular cancer. In this study, the risk of second cancer was not associated with the amount of diagnostic radiation exposure.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".