A Population-Based Analysis of Second Primary Cancers After Irradiation for Rectal Cancer
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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 it