Cancer patients' experience of receiving variant of uncertain significance results: An Asian perspective
Bibliographic record
Abstract
Due to a lack of ancestry-matched, functional, and segregation data, Asians have a higher rate of receiving a variant of uncertain significance (VUS) result following panel testing. Managing VUS results presents challenges, as it often leads to increased anxiety and distress among cancer patients undergoing genetic testing. This exploratory study aims to investigate the experience of Asian cancer patients upon receiving a VUS result. A qualitative, semi-structured interview study was conducted, involving cancer patients who had received a VUS result through the Cancer Genetics Service of the National Cancer Centre Singapore. Twenty participants were interviewed, and their responses were transcribed and analyzed using thematic analysis to identify key themes. Thematic analysis revealed five major themes: (1) VUS results are interpreted as uncertain outcomes; (2) a VUS result provides relief and prompts positive behavioral adjustments; (3) patients employ fatalism and religion as coping mechanisms to navigate uncertainty; (4) genetic counselors, family, and the community offer reassurance and support; (5) patients value updates on variant classifications for future management. While this novel study provides unique insights into the perspectives of Asian patients who receive VUS results, it also highlights patients' effective management of VUS results and uncertainty, which has implications for improving counseling practices in Asia. Emphasis must be placed on accurate interpretation and clear communication of VUS results to dispel the possibility of misconceptions, misdiagnosis, and mismanagement in cancer care.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".