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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".