Risk Factors for Locoregional Recurrence After Postmastectomy Radiotherapy in Breast Cancer Patients with Four or More Positive Axillary Lymph Nodes
Bibliographic record
Abstract
BACKGROUND: We investigated risk factors for locoregional recurrence (lrr) in breast cancer patients with 4 or more positive axillary lymph nodes receiving postmastectomy radiotherapy (pmrt). METHODS: Medical records (1998-2007) were retrospectively reviewed for the population of interest. The Kaplan-Meier method was used to calculate the survival rate; Cox regression models were used for univariate and multivariate analysis of predictors of breast cancer lrr. RESULTS: The study enrolled 439 patients. Median duration of follow-up was 54 months. The 5-year rates of locoregional recurrence-free survival (lrrfs), distant metastasis-free survival (dmfs), and breast cancer-specific survival (bcss) were 87.8%, 59.5%, and 70.7% respectively. In patients with lrr and no concomitant metastasis, and in those without lrr, the 5-year rates of dmfs were 21.1% and 65.7% respectively (p < 0.001), and the 5-year rates of bcss were 34.5% and 76.4% respectively (p < 0.001). Univariate analysis showed that menopausal status (p = 0.041), pN stage (p = 0.006), and positivity for her2 [human epidermal growth factor receptor 2 (p = 0.003)] or the triple-negative disease subtype (p < 0.001) were determinants of lrrfs. Multivariate analysis showed that pN3 stage [hazard ratio (hr): 2.241; 95% confidence interval (ci): 1.270 to 3.957; p = 0.005], her2 positivity (hr: 2.705; 95% ci: 1.371 to 5.335; p = 0.004), and triple-negative disease subtype (hr: 4.617; 95% ci: 2.192 to 9.723; p < 0.001) were independent prognostic factors of lrrfs. CONCLUSIONS: In breast cancer patients with 4 or more positive axillary lymph nodes who undergo pmrt for breast cancer, lrr significantly influences survival. Patients who developed lrr carried a high risk for distant metastasis and death. Pathologic stage (pN3), her2 positivity, and the triple-negative disease subtype are risk factors that significantly influence lrrfs.
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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 | 0.002 |
| 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) | 0.001 | 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 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".