Transformative, Patient-Centric Care: Digital Screening Program Provides Equitable Access to Breast Cancer Risk Assessment and Genetic Testing.
Bibliographic record
Abstract
OBJECTIVE: Breast cancer screening disparities persist in the United States, underscoring the need for universal tools to provide equitable care. This study analyzes two years of data from 15 Midstate Radiology Associates (MRA) sites through Connecticut, where all women were offered a digital risk stratification platform. The platform is offered in five different languages and was designed to identify individuals at an elevated risk for breast cancer and determine eligibility for genetic testing based on national guidelines. MATERIALS AND METHODS: A retrospective analysis was conducted across MRA sites from 1/1/2023 to 12/31/2024 to evaluate patient demographics. Before mammograms, all patients were invited to the Ambry CARE Program to assess breast cancer risk using the Tyrer-Cuzick (v8.0) algorithm and determine eligibility for genetic testing based on NCCN® guidelines for hereditary cancers conditions. We analyzed patient ethnicity, age, and language preferences for those who completed the digital screening tool. High-risk patients, those eligible for genetic testing, and individuals who underwent testing-including those with positive mutations-were compared. RESULTS: Between 1/1/2023 and 12/31/2024, 91,513 assessments were sent to patients via SMS, email, and kiosks, with each method opened over 95% of the time. 77,095 assessments were completed, achieving an 84.2% completion rate. The tool supported over 1,100 patients in languages like Spanish, Polish, Chinese, and Vietnamese. Patient ethnicity was recorded as: White (73%), Hispanic/Latino (11%), Black/African American (6%), Asian (4%), French Canadian/Cajun (3%), Ashkenazi Jewish (2%), and 1% each for other ethnicities. 52% of respondents were aged 51-70, and 8,025 women (10.4%) had a Tyrer-Cuzick score over 20%. Among women with elevated lifetime breast cancer risk: White (71%), Hispanic/Latino (7.2%), Black/African American (5%), Asian (1.8%), French Canadian/Cajun (3.4%), Ashkenazi Jewish (2.6%), and 9% for other ethnicities. 67% were aged 41-60. Of 20,063 women (26%) eligible for genetic testing based on NCCN® guidelines, 69.4% were White, 8.6% Hispanic/Latino, 4.6% Black/African American, 1.7% Asian, 3.2% French Canadian/Cajun, 3.6% Ashkenazi Jewish, and 8.9% other ethnicities. 52% were aged 51-70. 6,051 women chose genetic testing, with 451 (9%) receiving positive results. Age and ethnicity distributions were similar across these groups with increase in percentage of Latin/Hispanic opting for genetic testing. CONCLUSION: The digital tool effectively stratified breast cancer risk across diverse patient population, promoting equitable screening and education through multilingual options and personalized support.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".