Strategies of medical treatment for metastatic breast cancer (Review)
Bibliographic record
Abstract
Presently, metastatic breast cancer cannot be cured and therefore good palliation of symptoms and longer overall survival make the most important targets, always considering quality of life. In addition to the established hormonal therapies or chemotherapy regimens, several recent advances have accelerated progress in metastatic breast cancer treatment. As demonstrated in recent studies, the third-generation aromatase inhibitors are playing a significant role in the improvement of the therapeutic approach. The development of new drugs with novel mechanisms of action, such as taxanes, used alone in innovative schedules or in association with other drugs (mainly the anthracyclines), vinorelbine and gemcitabine, or capecitabine, which is administered orally, has broadened the scope of metastatic breast cancer chemotherapy. A new investigation field is represented by high-dose chemotherapy with stem cell support, which has provided controversial preliminary results but is also acknowledged to deserve larger and randomized trials. Finally, the emergence of biological therapies such as the anti-HER2 monoclonal antibody Trastuzumab opens new and exciting prospects for the treatment of this disease. Moreover, the present trend is to try to rationalize the therapeutic approach on the basis of biological parameters which are prognostic and predictive of treatment response.
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 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) | 0.002 | 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) | 0.008 | 0.006 |
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".