Screening with Magnetic Resonance Imaging in women at low and intermediate risk of breast Cancer
Bibliographic record
Abstract
In this cohort of 2995 women, 21 invasive epithelial cancers, one angiosarcoma and four cases of DCIS were identified over a two-year period. Of the invasive cancers, 20 were screen-detected and one was an interval cancer. Of the 21 invasive cancers detected in the cohort, 14 (67%) were less than 2 cm and 16 (76%) were node-negative. The sensitivity of MRI was 90%, the sensitivity of ultrasound was 62% and the sensitivity of mammography was 57%. The number of biopsies incurred by MRI (156) was far greater than the number incurred by either mammography (n = 35) or by ultrasound (n = 57). No cancer was identified by mammography that was not also identified by MRI, but one cancer was detected by ultrasound that was missed by MRI. Of the 19 cancers that were detected by MRI, 17 were also detected by ultrasound or mammography and two were detected by MRI alone. In terms of sensitivity, MRI is superior or similar to the combination of mammography and ultrasound for screening of women at low or intermediate risk of breast cancer. However, because of the additional costs incurred and the number of biopsies required in order to detect a few additional breast cancers, MRI screening is probably not warranted outside of high-risk populations.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".