A prognostic risk model for patients with triple negative breast cancer based on stromal natural killer cells, tumor‐associated macrophages and growth‐arrest specific protein 6
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Bibliographic record
Abstract
The aim of this study was to establish a prognostic risk model for patients with triple negative breast cancer (TNBC). A total of 278 specimens of human TNBC tissues were investigated by immunohistochemistry for growth-arrest specific protein 6 expression, infiltrations of stromal natural killer cells and tumor-associated macrophages. According to their prognostic risk scores based on the model, patients were divided into three groups (score 0, 1-2, 3). Correlations of prognostic risk scores, clinicopathologic features and overall survival (OS) were analyzed. To study the clinical value of this stratification model in early disease recurrence or metastasis, 177 patients were screened out for further analysis. Based on disease free survival (DFS), 90 patients fell within the DFS ≤3 years group and 87 patients within the DFS ≥5 years group. We analyzed the differences in prognostic risk scores between the two groups. The prognostic risk scores were negatively related to tumor size, lymph node metastasis and P53 status (P < 0.001 for all). Patients with low prognostic risk scores had longer OS (P = 0.001). Using multivariate analysis, it was determined that TNM stage (HR = 0.432, 95% confidence interval [CI] = 0.281-0.665, P = 0.003), FOXP3 positive lymphocytes (HR = 1.712, 95% CI = 1.085-2.702, P = 0.021) and prognostic risk scores (HR = 1.340, 95% CI = 1.192-1.644, P = 0.005) were independent prognostic factors for OS. Compared with the DFS ≥5 years group, the DFS ≤3 years group patients had significantly higher prognostic risk scores (P < 0.001). In conclusion, the prognostic risk score of the model was a significant indicator of prognosis for patients with TNBC.
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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 | 0.000 | 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 | 0.001 |
| 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 it