Contemporary Approaches to Breast Cancer Management: An Evidence Synthesis Guiding Clinical Practice and Patient Care.
Bibliographic record
Abstract
Background: Breast cancer remains the most frequently diagnosed cancer globally, though its management varies significantly across regions. This systematic review integrates recent evidence across six domains to delineate best practices for comprehensive care. Methods: A systematic literature search was conducted across MEDLINE, Embase, Cochrane Library, Web of Science, and CINAHL (2010–2024), in line with PRISMA 2020 reporting standards. Eligible studies were screened by two reviewers. Quality was assessed using validated tools appropriate to study design, including Cochrane RoB 2.0, Newcastle-Ottawa Scale, AMSTAR-2, and AGREE II. Evidence was synthesized narratively and appraised using the GRADE framework. Results: Key advances include the application of molecular profiling in tailoring therapy, treatment de-intensification for selected low-risk groups, escalation for aggressive subtypes, and improved multidisciplinary decision-making. Hypofractionated radiotherapy has shown comparable efficacy with reduced side effects, while genomic testing helps identify patients who can safely avoid chemotherapy. Targeted therapies have substantially improved outcomes in specific subgroups. Unique strategies are needed for elderly, male, and pregnant patients, and oligometastatic disease is increasingly approached with curative intent. Conclusion: Precision medicine has redefined breast cancer treatment, emphasizing individualized and integrated multidisciplinary strategies. Implementation frameworks that minimize disparities and maximize both survival and quality of life outcomes are necessary to put this evidence into practice.
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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 | 0.066 | 0.159 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".