Standard and Genomic Tools for Decision Support in Breast Cancer Treatment
Bibliographic record
Abstract
Over the past few decades, comprehensive characterization of the cancer genome has elucidated pathways that drive cancer and mechanisms of resistance to therapy and provided important insights for development of new therapies. These advances have resulted in the development of prognostic and predictive tools for use in clinical settings, which can assist clinicians and patients in making informed decisions about the benefits of established therapies. In early-stage breast cancer, multiparameter genomic assays are now available for decision making about the duration of adjuvant endocrine therapy and the use of adjuvant chemotherapy. Similarly, in metastatic disease, there are multiple commercially available next-generation sequencing options for identifying genetic alterations in tumors that may be targeted with a drug. Although these tools hold great promise for providing precision medicine, it can be difficult for the treating physician to evaluate their clinical utility and appropriately select tools for individual clinical situations. This review summarizes the currently available genomic tools in breast cancer, the data underlying their clinical validity and utility, and how they can be used in conjunction with standard clinicopathologic data for making adjuvant and metastatic treatment decisions.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".