BRCA-DIRECT digital pathway for diagnostic germline genetic testing within a UK breast oncology setting: a randomised, non-inferiority trial
Bibliographic record
Abstract
ABSTRACT BACKGROUND Genetic testing to identify germline high-risk pathogenic variants in breast cancer susceptibility genes is an important step in the breast cancer diagnostic pathway. To expand capacity and reduce turnaround time, testing is increasingly offered within ‘mainstream’ oncology services, rather than via referral to clinical genetics. However, mainstream capacity is also stretched, as testing is offered to greater proportions of patients. Novel patient-centred pathways may offer opportunity for improved access. PATIENTS AND METHODS We recruited 1,140 women with unselected breast cancer to undergo germline genetic testing through the BRCA-DIRECT digital pathway; compromising at-home saliva sampling and consenting, with access to a digital dashboard to complete tasks and a genetic counselling telephone hotline. Ahead of consenting to the test, participants were randomised to receive information about genetic testing digitally (569/1140, 49.9%) or via a pre-test genetic counselling consultation (571/1140, 50.1%). The primary outcome was uptake of genetic testing. We also measured patient knowledge, anxiety, and satisfaction, and conducted a healthcare professional survey. RESULTS 1,001 (87.8%) participants progressed to receive their pre-test information and consented to testing. Uptake was higher within participants randomised to receive digital information compared with those randomised to a pre-test genetic counselling consultation (90.8% (95% CI: 88.5% to 93.1%) vs 84.7% (95% CI: 81.8% to 87.6%), p=0.002, adjusted for participant age and site). Non-inferiority was observed in relation to all other outcomes evaluated. Usage of the telephone hotline was modest (<20% of participants; 1,441 total minutes, 344 clinical minutes recorded) and, of 37 healthcare professionals surveyed, there was majority agreement that all elements of the pathway were equivalent to current standard-of-care. CONCLUSION Findings demonstrate that standardised, digital information offers a non-inferior alternative to conventional genetic counselling consultation, and that an end-to-end patient-centred, digital pathway (supported by genetic counselling hotline) could feasibly be implemented into mainstream breast oncology settings.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".