Breast Cancer Metastasis: Advances Through the Use of In Vitro Co-Culture Model Systems
Bibliographic record
Abstract
Worldwide, breast cancer is the most frequent cancer diagnosed in women and is the second-most leading cause of cancer related deaths in women (Jemal, Bray et al. 2011). Death from breast cancer is most often the result of the spread of the primary tumour to distant sites, where the cancer cells lodge and develop into metastases. Depending on the site of the metastasis, the patient may live for years with reduced quality of life and needing increased health care resources. There is clearly a need for a greater understanding of the molecular events involved in breast cancer metastasis in order to improve treatment options for breast cancer patients and develop therapies aimed at preventing breast cancer metastasis. Here we will summarize what is known about the molecular basis of breast cancer metastasis and discuss the use of in vivo and primarily in vitro model systems to study it.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".