Attitudes regarding polygenic risk testing for lung cancer: a mixed-methods study
Bibliographic record
Abstract
BACKGROUND: Polygenic risk scores (PRS) hold promise for early lung cancer detection and personalized treatment, yet factors influencing patient interest in PRS-based genetic testing are not well understood. PURPOSE: Grounded in the health belief model, this mixed-methods study explored knowledge, attitudes, perceived benefits and barriers to lung cancer PRS, and preferences for receiving PRS results. RESULTS: The study included 141 individuals (41% African American, 63% female) recruited from two hospital affiliates of a comprehensive cancer center in the Southwestern United States. Although participants recognized the severity of lung cancer, knowledge of PRS was limited. Concerns about privacy, psychological impacts, and uncertainty about result usefulness diminished interest in genetic testing for polygenic risk. Significant differences (P < .05) in attitudes were observed: women expressed heightened concerns about psychological effects, and African Americans reported greater perceptions of stigma and concerns about potential familial consequences. Qualitative findings emphasized the psychological burden of learning one's genetic risk, particularly among those with family cancer histories or smoking exposure. Participants emphasized the need for clear, actionable results and assurances of data privacy. CONCLUSIONS: Perceived benefits and barriers to PRS-based testing varied by sociodemographic and personal risk factors, with concerns about stigma, psychological burden, and privacy shaping attitudes. Given participants' emphasis on clear, actionable results, strategies to enhance uptake should improve risk communication, ensure data privacy, and provide guidance on risk-reducing actions. Tailored approaches addressing subgroup-specific concerns may improve diverse patient engagement and equitable access to PRS.
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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 | 0.028 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".