Screening women at high risk for breast cancer with mammography and magnetic resonance imaging
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: The authors compared the performance of screening mammography versus magnetic resonance imaging (MRI) in women at genetically high risk for breast cancer. METHODS: The authors conducted an international prospective study of screening mammography and MRI in asymptomatic, genetically high-risk women age >/= 25 years. Women with a history of breast cancer were eligible for a contralateral screening if they had been diagnosed within 5 years or a bilateral screening if they had been diagnosed > 5 years previously. All examinations (MRI, mammography, and clinical breast examination [CBE]) were performed within 90 days of each other. RESULTS: In total, 390 eligible women were enrolled by 13 sites, and 367 women completed all study examinations. Imaging evaluations recommended 38 biopsies, and 27 biopsies were performed, resulting in 4 cancers diagnosed for an overall 1.1% cancer yield (95% confidence interval [95%CI], 0.3-2.8%). MRI detected all four cancers, whereas mammography detected one cancer. The diagnostic yield of mammography was 0.3% (95%CI, 0.01-1.5%). The yield of cancer by MRI alone was 0.8% (95%CI, - 0.3-2.0%). The biopsy recommendation rates for MRI and mammography were 8.5% (95%CI, 5.8-11.8%) and 2.2% (95%CI, 0.1-4.3%). CONCLUSIONS: Screening MRI in high-risk women was capable of detecting mammographically and clinically occult breast cancer. Screening MRI resulted in 22 of 367 of women (6%) who had negative mammogram and negative CBE examinations undergoing biopsy, resulting in 3 additional cancers detected. MRI also resulted in 19 (5%) false-positive outcomes, which resulted in benign biopsies.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it