Bibliographic record
Abstract
Breast cancer is a complex disease that comprises cancers of distinct biologies and responses to treatment. Clinical management relies on traditional clinicopathological parameters, involving lymph node status, histological grade, as well as expression of the estrogen receptor or human epidermal growth factor receptor 2. Molecular pathology as well as protein and gene expression profiling have divided breast tumors into molecular subtypes associated with different clinical outcomes. One of these, defined as basal breast cancer, is associated with poor prognosis. Molecular mechanisms involved in the induction of basal breast cancer are poorly understood and targeted therapies for this subtype are lacking. Recent evidence using murine models identified a role for the Met receptor tyrosine kinase in the induction of murine mammary tumors with characteristics of human basal breast cancers. Moreover, elevated Met protein and RNA is associated with human basal tumors and poor outcome. These studies identify a link between the Met receptor tyrosine kinase, epithelial mesenchymal transition, and basal breast cancer. In this review, we provide an overview of murine Met models in relation to the spectrum of mouse models of breast cancer and a role for the Met receptor in basal breast cancer tumorigenesis.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".