Irradiation after Surgery for Breast Cancer Patients with Primary Tumours and One to Three Positive Axillary Lymph Nodes: Yes or No?
Bibliographic record
Abstract
OBJECTIVE AND METHODS: We retrospectively analyzed clinicopathologic features and survival in breast cancer patients who had T1 or T2 primary tumours and 1-3 histologically involved axillary lymph nodes and who were treated with modified radical mastectomy without adjuvant radiotherapy (rt). We also explored prognosis to find the high- and low-risk groups. RESULTS: From May 2001 to April 2005, 368 patients treated at Tianjin Tumor Hospital met the study criteria. The 5- and 8-year rates were 7.2% and 10.7% for locoregional recurrence (lrr), 85.1% and 77.7% for disease-free survival (dfs), and 92.8% and 89.3% for overall survival (os). Multivariate Cox regression analysis showed that age, tumour size, estrogen receptor (er) status, and lymphovascular invasion (lvi) were independent prognostic factors for lrr and dfs. Based on 4 patient-related factors that indicate poor prognosis (age < 40 years, tumour > 3 cm, er negativity, and lvi), the high-risk group (patients with 3 or 4 factors, accounting for 12.5% of the cohort) had 5- and 8-year rates of 24.3% and 36.9% for lrr, 57.2% and 39.2% for dfs, and 74.8% and 43.8% for os compared with 5.0% and 7.1% for lrr, 88.9% and 83.1% for dfs, 91.6% and 83.4% for os in the low-risk group (patients with 0-2 factors, accounting for 87.5% of the cohort; p < 0.001). CONCLUSIONS: Our study identified several risk factors that correlated independently with a greater incidence of lrr and distant metastasis in patients with T1 and T2 breast cancer and 1-3 positive nodes. Patients with 0-2 risk factors may not be likely to benefit from post-mastectomy rt, but patients with 3-4 risk factors may need rt to optimize locoregional control and improve survival.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".