Predictive Factors of Response to Neoadjuvant Chemotherapy (NACT) and Immune Checkpoint Inhibitors in Early-Stage Triple-Negative Breast Cancer Patients (TNBC)
Bibliographic record
Abstract
Triple-negative breast cancer (TNBC) is a heterogenous group of breast tumors. This type of breast tumor is relatively difficult to manage, due to the lack of expression of Hormone Receptors (HR) and human epidermal growth factor receptor (HER2). Efforts have been made to understand the factors involved in determining how a triple-negative breast tumor responds to therapy. The standard of treatment in most cases today is a combined modality of immune checkpoint inhibitors (ICIs) and chemotherapy with agents such as anti-mitotic (taxanes) or DNA-damaging agents (alkylating agents, cyclophosphamides, platin salts). In this study, we investigated the predictive and prognostic factors for TNBC, in the neoadjuvant setting; understanding each patient's response before treatment initiation is crucial to guiding the subsequent approach and finally improving patient outcomes. We focused on tumor-infiltrating lymphocytes at the site of the primary tumor (TILs), circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), the mutational status of protein 53 (p53), and Ki-67, investigating the potential roles of these factors in predicting responses to anti-cancer agents.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".