Prognostic role of radiotherapy in low-risk elderly breast cancer patients after breast-conserving surgery: a cohort study
Bibliographic record
Abstract
Background: Previous research suggested that radiotherapy (RT) had a small absolute benefit in patients with low-risk breast cancer over the age of 65. To reduce the patient's treatment burden and cost, as well as the damage to normal tissue, this study sought to explore the prognostic role of RT after breast-conserving surgery (BCS) in elderly patients. Methods: ) were included in this study. Age, marital status, histology, race, grade, human epidermal growth factor receptor 2 (HER2), subtype, treatment method, and survival were also collected from the Surveillance, Epidemiology, and End Results (SEER) database from 2004 to 2015. We compared overall survival (OS) and breast cancer-specific survival (BCSS) before and after propensity score matching (PSM) in the patients who underwent BCS with or without RT. Kaplan-Meier method and Cox proportional hazards regression analyses were used in our study. Results: The data of 3,623 patients were analyzed in this study. Among them, 2,851 (78.69%) patients had received RT. The multivariate analyses before PSM showed that RT resulted in better OS [hazard ratio (HR) 0.51, 95% confidence interval (CI): 0.42-0.62, P<0.001], and BCSS (HR 0.40, 95% CI: 0.27-0.58, P<0.001). The multivariate analyses after PSM (n=1,538) confirmed that patients who received RT (n=769) had a longer survival time than those who did not (n=769) (OS: HR 0.73, 95% CI: 0.57-0.95, P=0.018; and BCSS: HR 0.57, 95% CI: 0.35-0.93, P=0.025). The survival analysis showed that patients receiving RT had a better OS (P=0.028) and BCSS (P=0.016) than those who did not receive RT. However, there were no significant differences in patients' OS and BCSS with or without RT across the different age subgroups (P>0.05). Conclusions: breast cancer. Furthermore, at the age of 65-69 years, the P value for OS approached 0.05, which suggests that the decision to administer RT in this patient group should be made based on each patient's condition.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".