Traceback: A Proposed Framework to Increase Identification and Genetic Counseling of <i>BRCA1</i> and <i>BRCA2</i> Mutation Carriers Through Family-Based Outreach
Bibliographic record
Abstract
In May 2016, the Division of Cancer Prevention and the Division of Cancer Control and Population Sciences, National Cancer Institute, convened a workshop to discuss a conceptual framework for identifying and genetically testing previously diagnosed but unreferred patients with ovarian cancer and other unrecognized BRCA1 or BRCA2 mutation carriers to improve the detection of families at risk for breast or ovarian cancer. The concept, designated Traceback, was prompted by the recognition that although BRCA1 and BRCA2 mutations are frequent in women with ovarian cancer, many such women have not been tested, especially if their diagnosis predated changes in testing guidelines. The failure to identify mutation carriers among probands represents a lost opportunity to prevent cancer in unsuspecting relatives through risk-reduction intervention in mutation carriers and to provide appropriate reassurances to noncarriers. The Traceback program could provide an important opportunity to reach families from racial, ethnic, and socioeconomic groups who historically have not sought or been offered genetic counseling and testing and thereby contribute to a reduction in health disparities in women with germline BRCA mutations. To achieve an interdisciplinary perspective, the workshop assembled international experts in genetics, medical and gynecologic oncology, clinical psychology, epidemiology, genomics, cost-effectiveness modeling, pathology, bioethics, and patient advocacy to identify factors to consider when undertaking a Traceback program. This report highlights the workshop deliberations with the goal of stimulating research and providing a framework for pilot studies to assess the feasibility and ethical and logistical considerations related to the development of best practices for implementation of Traceback studies.
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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 | 0.037 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.008 | 0.013 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".