Psychological and emotional impacts of communicating breast cancer risk using multifactorial assessment with polygenic risk score: Findings from PERSPECTIVE I&I
Bibliographic record
Abstract
PURPOSE: To examine the psychological and emotional outcomes of personalized breast cancer risk communication up to 1 year after disclosure in a risk-stratified breast screening preimplementation study (Personalized Risk Assessment for Prevention and Early Detection of Breast Cancer: Integration and Implementation). METHODS: Among 3753 females aged 40 to 69, unaffected by breast cancer, with a prior mammogram, and who underwent multifactorial risk assessment to estimate their 10-year breast cancer risk, 2734 completed follow-up questionnaires up to 1 year after risk communication: 78.5% were at average risk, 16.5% at higher than average risk, and 5.0% at high risk. The impact of risk communication on breast cancer worry and psychological distress and factors associated with decisional regret were examined. RESULTS: Breast cancer worry and psychological distress scores remained low after risk communication and at 1 year follow-up. Up to 1 year after disclosure, small significant differences in breast cancer worry were observed between risk levels. Decisional regret was very low 1 year after risk communication. Lower levels of decisional regret were significantly associated with some factors, including higher satisfaction with the information received. CONCLUSION: This study suggests that personalized breast cancer risk communication has low negative psychological and emotional effects and highlights the importance of the information received for implementing this approach at population level.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".