Supporting Genomic Testing in Breast, Ovarian and Endometrial Cancer
Bibliographic record
Abstract
Precision oncology has fundamentally transformed cancer care by enabling targeted therapies and immunotherapies guided by tumor and germline biomarkers. The inclusion of comprehensive biomarker testing (CBT) in clinical guidelines now applies to most solid tumors which account for almost half a million deaths in the United States each year. Despite rapid advances in molecular diagnostics and expansion of clinical guidelines, substantial gaps and challenges persist in the delivery of guideline-recommended germline, tumor molecular, and immune biomarker testing across disease sites. System-level strategies to proactively identify eligible patients are limited and increasingly unable to operationalize the rapidly evolving complexity of CBT guidelines. Many patients have limited knowledge about CBT and feel unpreparted to recognize how testing could influence treatment decisions or discuss testing. These barriers can limit patient-centered communication and contribute to missed opportunities for guideline concordant care. Oncologists face substantial challenges to delivering guideline-concordant CBT, including competing demands, fragmented workflows, and limited mechanisms for incorporating patients’ questions, concerns and testing priorities into routine care encounters. Together, these barriers underscore the urgent need for scalable, multicomponent approaches that improve the delivery of precision oncology care. Building on compelling preliminary work, the PRECISE (Patient-centered pREcision oncology Communication and Implementation Support to Enhance Biomarker Testing) Trial targets barriers at the patient, clinician, and health system levels. PRECISE will address persistant gaps in CBT delivery by integrating EHR-based identification of patients who meet NCCN testing criteria, personalized AI-enabled patient activation, and clinician communication support into the routine oncology workflows. Guided by the Patient Centered Communication and Ottawa Decision Support Frameworks and Practical, Robust, Implementation and Sustainability Model, we will recruit 320 patients from two academic-community clinical networks. Following rigorous patient- and clinician-informed optimization, we will conduct a two-arm randomized trial comparing PRECISE with enhanced usual care. Specific aims will assess PRECISE’s effects on receipt of guideline-concordant CBT and patient-reported outcomes related to knowledge and treatment decision self-efficacy, which may inform subsequent treatment decisions by patients and clinicians. A mulitisite process evaluation will examine implementation from patient, clinician and system perspectives. If effective, PRECISE will provide a scalable and potentially practice-changing model for integrating EHR-enable case identification, AI-supported patient activation, clinician communication, and workflow-based implementation strategies to ensure that eligible patients can benefit from precision oncology 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".