Systemic treatment and breast cancer outcomes over time: a systematic review of observational studies
Bibliographic record
Abstract
Background: Significant advancements have transformed the clinical management of breast cancer (BC), which has become complex. This review aims to provide an overview of how systemic BC treatment strategies have evolved and affected survival in clinical practice. Methods: Studies were identified through PubMed, EMBASE, and Web of Science from 2011 up to April 2024. Cohort studies comparing treatment strategies and their effects on overall survival (OS), disease-free survival (DFS), and invasive disease-free survival (iDFS) were included. Results: A total of 25 studies, with 74,775 patients, were included. Treatments compared included chemotherapy (76%), endocrine therapies (60%), and anti-HER2 therapies (16%). Among chemotherapies, studies comparing taxanes, anthracyclines, and platinum-based chemotherapy in diverse BC subtypes found that only the latter improved DFS in BRCA1 mutation carriers. Aromatase inhibitors (AIs) plus ovarian function suppression showed better outcomes than selective estrogen receptor modulators. AIs also improved iDFS compared to tamoxifen. Trastuzumab with chemotherapy significantly improved OS in HER2-positive patients compared to chemotherapy alone. Conclusions: This review underscores a notable shift towards the use of combined therapies in BC treatment, which appears to be linked to better survival outcomes. Observational studies offer valuable insights into the variability of survival rates and the growing complexity of treatment strategies. These types of studies should be given thoughtful consideration in future clinical decision-making and in shaping treatment guidelines.
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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) | 0.000 | 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 | 0.000 |
| 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".