A Review of Therapeutic Antibodies in Breast Cancer
Bibliographic record
Abstract
Since the first production of monoclonal antibodies about 35 years ago, researchers have found them useful in the treatment and diagnosis of various diseases such as cancer. By developing different types of monoclonal antibodies such as humanized, drug conjugated, or bispecific ones, researchers, have achieved remarkable success in treating several complicated and challenging diseases, targeting specific antigens or receptors makes monoclonal antibodies the right choice to inhibit signaling pathways like programmed death-ligand 1 (PD-L1) or programmed death1 (PD-1) and changing cell behavior. As one of the most common types of malignancies among women, breast cancer is one of the most critical conditions which different types of monoclonal antibodies were designed and produced to treat. Therefore, we reviewed these antibodies in breast cancer, their targets, and their efficacy and toxicity, with more focus on recent PD-L1or PD-1 inhibitor antibodies in breast cancer and beyond.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".