Screening women at high risk for breast cancer with mammography and magnetic resonance imaging
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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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".