Oncolytic virus-based combination therapy in breast cancer
Bibliographic record
Abstract
Breast cancer continues to pose significant challenges in the field of oncology, necessitating innovative treatment approaches. Among these, oncolytic viruses have emerged as a promising frontier in the battle against various types of cancer, including breast cancer. These viruses, often genetically modified, have the unique ability to selectively infect and destroy cancer cells while leaving healthy cells unharmed. Their efficacy in tumor eradication is not only owing to direct cell lysis but also relies on their capacity to activate the immune system, thereby eliciting a potent and sustained antitumor response. While oncolytic viruses represent a significant advancement in cancer treatment, the complexity and adaptability inherent to cancer require a diverse array of therapies. The concept of combining oncolytic viruses with other treatment modalities, such as chemotherapy, immunotherapy, and targeted therapies, has received significant attention. This synergistic approach capitalizes on the strengths of each therapy, thus creating a comprehensive strategy to tackle the heterogeneous and evolving nature of breast cancer. The purpose of this review is to provide an in-depth discussion of preclinical and clinical viro-based combination therapy in the context of breast cancer.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".