Supporting Genomic Testing in Breast, Ovarian and Endometrial Cancer
Bibliographic record
Abstract
Almost 400,000 American women will be diagnosed with breast, ovarian or endometrial cancer in 2024, accounting for over 68,000 deaths. Tumor and germline genomic testing (GT) have become standard of care for these cancers through the rapid transformation of how oncologists characterize and treat cancers. Not only have these cancers long been implicated in several hereditary cancer syndromes but clinical guidelines also now integrate the wide use of GT to provide more precise information about effective therapeutic options and prognosis. Despite continued dissemination of the guidelines, many challenges remain to maximize the benefits of GT and refined treatment selection. Many patients do not receive GT and have a poor understanding of the GT process, their results and its impact of treatment selection. Supporting patients to take an active role in their care through effective patient education and activation, and patient-provider communication improves patient-reported and clinical outcomes. Our prior work demonstrates that integration of patient education, values clarification, and patient activation methods support patient-centered communication and improve knowledge and decision self-efficacy and reduce decisional conflict related to treatment. We build on this compelling prior work to test a digital health tool for integrated tumor and germline testing (Genomic EducatioN and Navigation Assistant; GENNA) to support clinical, decisional, and communication outcomes in breast, ovarian and endometrial cancer. Guided by the Patient Centered Communication Framework and the Ottawa Decision Support Framework, the tool’s components include education, values clarification about treatment decisions, and a question prompt list that can be personalized to address patient concerns and knowledge gaps. We will recruit 400 women who are newly diagnosed or have recently progressed with breast, ovarian and endometrial cancers for whom guidelines recommend GT. They will be recruited from multiple clinic sites within two large academic and community clinical networks in the Mid-Atlantic US, serving diverse patient populations. Our work will be conducted in two phases. In Phase 1, we will refine intervention elements using patient engagement and Learner Verification and Revision methods. In Phase 2, we will conduct a 2-arm randomized trial to compare GENNA vs. usual care. Specific aims are to assess intervention effects on patient-reported outcomes related to therapeutic decision-making and receipt of guideline-based germline and tumor testing and conduct a multi-level and multi-site process evaluation of care delivery from the patient, clinician and system perspective to inform future dissemination. If effective, our strategy will provide a scalable approach to support the expanding group of cancer patients eligible to receive GT and guideline-based care and ultimately impact morbidity and mortality. Methods can expand to other disease sites for which testing has become standard of care.
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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 | 0.012 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".