A Population-Based Analysis of Second Primary Cancers After Irradiation for Rectal Cancer
Bibliographic record
Abstract
OBJECTIVE: To investigate the possible association between pelvic irradiation for rectal cancer and subsequent second primary cancers. PATIENTS AND METHODS: A population-based analysis of 20,910 individuals with rectal cancer from the Surveillance, Epidemiology, and End Results registry, for whom follow-up times were at least 5 years, was performed. Kaplan-Meier estimates for the development of second cancers within irradiated and nonirradiated cohorts provided a comparison that accounted for censored data. Cox proportional hazards analyses were further conducted to compensate for patient and tumor-related factors. RESULTS: A total of 656 (12%) and 2368 (16%) second primary cancers were enumerated from the irradiated and nonirradiated cohorts, respectively, with the proportion of second primary cancers within the irradiated cohort being significantly decreased (P < 0.001) on crude analysis. However, Kaplan-Meier and Cox analyses revealed no significant difference between the 2 cohorts when all second primary cancer sites were considered together (hazard ratio = 1.02; 95% confidence interval [CI], 0.92-1.12). Proportional hazards analysis for specific second primary sites revealed a decreased risk after pelvic irradiation for cancer of the prostate (hazard ratio = 0.63; 95% CI, 0.48-0.84), and an increased risk for cancers of the uterine corpus & cervix (hazard ratio = 2.5; 95% CI, 1.6-4.0). CONCLUSION: Second primary cancers after irradiation for rectal cancers appear relatively infrequent compared with the background incidence of spontaneous cancers, and should not factor into treatment decisions for this older population. We hypothesize that the incidence of second primary tumors within adjacent organs could represent a balance between the radiation-induction of tumors and the radiation-inhibition of spontaneously occurring tumors.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".