An eHealth Delivery Alternative for Cancer Genetic Testing for Hereditary Predisposition in Patients With Metastatic Cancers: Protocol for a Randomized Trial
Bibliographic record
Abstract
BACKGROUND: Germline BRCA1 and BRCA2 testing is a standard evidence-based practice, with established risk reduction and cancer screening guidelines for genetic carriers. With Food and Drug Administration approval for poly (adenosine diphosphate ribose) polymerase (PARP) inhibitors in patients with metastatic breast, ovarian, pancreatic, and prostate cancer, there is an additional therapeutic rationale for testing all patients with these cancers for germline BRCA1 and BRCA2 mutations. However, many at-risk patients do not have access to genetic services, leaving many genetic carriers unidentified. OBJECTIVE: The eREACH (A Randomized Study of an eHealth Delivery Alternative for Cancer Genetic Testing for Hereditary Predisposition in Metastatic Breast, Ovarian, Prostate, and Pancreatic Cancer Patients) study evaluates the effectiveness of a theoretically and stakeholder-informed eHealth (eg, digital) delivery alternative to traditional genetic counseling for patients with metastatic breast or prostate cancer or advanced or metastatic ovarian or pancreatic cancer referred for genetic testing to determine whether they are candidates for a PARP inhibitor. METHODS: The eREACH study is a randomized noninferiority study using a 2 × 2 design to test a self-directed digital intervention to deliver clinical genetic testing for patients with metastatic cancers. The traditional standard-of-care pretest (visit 1) and posttest (visit 2-disclosure) counseling delivered by a genetic counselor is replaced with our patient-informed digital intervention. The four arms were as follows: arm A, genetic counselor for visits 1 and 2; arm B, genetic counselor for visit 1 and digital intervention for visit 2; arm C, digital intervention for visit 1 and genetic counselor for visit 2; and arm D, digital intervention for both visits. Participants were adults with advanced or metastatic breast, ovarian, pancreatic, and prostate cancer. The primary outcomes of this study were change in genetic knowledge and anxiety from baseline to postdisclosure assessment. We will test whether the digital intervention is noninferior to standard-of-care counseling with a genetic counselor using a modified noninferiority ANOVA of the posttest disclosure minus baseline change scores. In secondary analyses, we will test pairwise differences among the 4 groups. RESULTS: As of January 2025, we have completed enrollment of 229 participants. Data analysis is ongoing, and we expect the results to be published in 2025. CONCLUSIONS: Increasing indications for BRCA1 and BRCA2 testing create a pressing need to evaluate alternative delivery models to increase access and uptake of these tests while maintaining adequate patient cognitive, affective, and behavioral outcomes. The eREACH study evaluates the effectiveness of an interactive, patient-centered digital intervention to deliver clinical genetic testing to patients with metastatic cancers. We expect that this work will inform evidence-based guidelines and the standard of care for delivery of genetic testing, and it is designed to be broadly applicable and easily adaptable for other populations and settings even beyond oncology. TRIAL REGISTRATION: ClinicalTrials.gov NCT04353973; https://clinicaltrials.gov/study/NCT04353973. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/72515.
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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 | 0.024 | 0.027 |
| Meta-epidemiology (narrow) | 0.006 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.112 | 0.017 |
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".