Assessing Individual Breast Cancer Risk within the U.K. National Health Service Breast Screening Program: A New Paradigm for Cancer Prevention
Bibliographic record
Abstract
The aim of this study is to determine breast cancer risk at mammographic screening episodes and integrate standard risk factors with mammographic density and genetic data to assess changing the screening interval based on risk and offer women at high risk preventive strategies. We report our experience of assessing breast cancer risk within the U.K. National Health Service Breast Screening Program using results from the first 10,000 women entered into the "Predicting Risk Of breast Cancer At Screening" study. Of the first 28,849 women attending for screening at fifteen sites in Manchester 10,000 (35%) consented to study entry and completed the questionnaire. The median 10-year Tyrer-Cuzick breast cancer risk was 2.65% (interquartile range, 2.10-3.45). A total of 107 women (1.07%) had 10-year risks 8% or higher (high breast cancer risk), with a further 8.20% having moderately increased risk (5%-8%). Mammographic density (percent dense area) was 60% or more in 8.3% of women. We collected saliva samples from 478 women for genetic analysis and will extend this to 18% of participants. At time of consent to the study, 95.0% of women indicated they wished to know their risk. Women with a 10-year risk of 8% or more or 5% to 8% and mammographic density of 60% or higher were invited to attend or be telephoned to receive risk counseling; 81.9% of those wishing to know their risk have received risk counseling and 85.7% of these were found to be eligible for a risk-reducing intervention. These results confirm the feasibility of determining breast cancer risk and acting on the information in the context of population-based mammographic screening.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".