Combined hormonal therapy for advanced breast cancer in postmenopausal patients
Bibliographic record
Abstract
The remarkable generation of scores of increasingly sophisticated mouse models of mammary cancer over the past two decades has provided tremendous insights into molecular derangements that can lead to cancer.The relationships of these models to human breast cancer, however, remain problematic.Recent advances in genomic technologies offer significant opportunities to identify critical changes that occur during cancer evolution and to distinguish in a complex and comprehensive manner the key similarities and differences between mouse models and human cancer.Comparisons between mouse and human tumors are being performed using comparative genomic hybridization, gene expression profiling, and proteomic analyses.The appropriate use of genetically engineered mouse models of mammary cancer in preclinical studies remains an important challenge which may also be aided by genomic technologies.Genomic approaches to cancer are generating huge datasets that represent a complex system of underlying networks of genetic interactions.Mouse models offer a tremendous opportunity to identify such networks and how they relate to human cancer.The challenge of the future remains to decipher these networks in order to identify the genetic nodes of oncogenesis that may be important targets for chemoprevention and therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".