The Role of Magnetic Resonance Imaging in Screening Women at High Risk of Breast Cancer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Most women at very high risk of breast cancer because of a mutation in the genes BRCA1 or BRCA2, or a very strong family history of breast cancer, opt for intensive breast screening rather than bilateral prophylactic mastectomy. Annual screening mammography has low sensitivity in this population in part because of the greater breast density and faster tumor growth of younger women, resulting in cancers being detected at a suboptimal stage. In 11 prospective comparative studies, the addition of annual contrast-enhanced magnetic resonance imaging (MRI) of the breast to mammography demonstrated more than 90% sensitivity, more than twice that of mammography alone. False-positive rates were higher with the addition of MRI, but specificity improved on successive rounds of screening. Although survival data are not yet available, the stage distribution of these tumors predicts a significant reduction in breast cancer mortality rate compared with that of screening without MRI. Accordingly, annual MRI plus mammography is now the standard of care for screening women aged 30 years or older who are known or likely to have inherited a strong predisposition to breast cancer (based on the above evidence) and for women who received radiation therapy to the chest before the age of 30 years (based on expert opinion). Further research is necessary to define the optimal screening schedule for different subgroups. Formal studies of other high-risk populations (eg, biopsy showing lobular neoplasia or atypical ductal hyperplasia, dense breasts, and personal history of breast cancer at a young age) should be done before MRI screening is routinely adopted for these women.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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