The Development and Evaluation of Novel Patient Educational Material for a Variant of Uncertain Significance (VUS) Result in Hereditary Cancer Genes
Bibliographic record
Abstract
A Variant of Uncertain Significance (VUS) is a difference in the DNA sequence with uncertain consequences for gene function. A VUS in a hereditary cancer gene should not change medical care, yet some patients undergo medical procedures based on their VUS result, highlighting the unmet educational needs among patients and healthcare providers. To address this need, we developed, evaluated, and refined novel educational materials to explain that while VUS results do not change medical care, it remains important to share any personal or family history of cancer with family members given that their personal and family medical history can guide their cancer risk management. We began by reviewing the prior literature and transcripts from interviews with six individuals with a VUS result to identify content and design considerations to incorporate into educational materials. We then gathered feedback to improve materials via a focus group of multidisciplinary experts and multiple rounds of semi-structured interviews with individuals with a VUS result. Themes for how to improve content, visuals, and usefulness were used to refine the materials. In the final round of interviews with an additional 10 individuals with a VUS result, materials were described as relatable, useful, factual, and easy to navigate, and also increased their understanding of cancer gene VUS results.
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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 | 0.016 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".