Breast Cancer Genetic Testing: More Than a Medical Management Tool
Bibliographic record
Abstract
BACKGROUND: Knowing whether a harmful hereditary mutation exists in BRCA1 and BRCA2 can enable women to make informed decisions regarding surveillance and surgery options to manage risk. Given the attention in the media about BRCA genetic testing, nurses need to revisit how this knowledge may affect a woman's sense of self and the forces that may influence this decision. OBJECTIVES: This article aims to understand how complex the decision to undergo genetic testing may be for some women by exploring the impact of genetic knowledge on the self, changes to customary definitions for health and illness, and ethical issues and social forces that may influence genetic testing decisions. METHODS: A review of the literature was undertaken to understand how genetic knowledge may alter meanings attached to the breast and how health is defined, and to identify ethical concerns and social forces that may affect a woman's decision to undergo or decline an offer for genetic testing. FINDINGS: An understanding and awareness of the potential benefits and harms of BRCA1 and BRCA2 genetic testing, as well as the social forces that may influence a woman's decision to undergo or decline an offer for genetic testing and the commitment to remain open to the uniqueness of each woman's situation, may enhance the nurse-patient relationship and result in a decision that is ethically in the best interest of the patient.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".