Preoperative Breast Magnetic Resonance Imaging: Applications in Clinical Practice
Bibliographic record
Abstract
Results of large randomized trials have shown that survival rates after breast conserving surgery are equivalent to those obtained by radical mastectomy. Breast conserving surgery with wide local excision in women with early stage breast cancer who are thought to have a single and resectable tumour as determined by clinical examination and conventional imaging followed by postoperative irradiation is the standard of care in early breast cancer. Mapping of local disease is the key element to guide optimal surgery to obtain tumour-free margins, thereby decreasing risk of local recurrence. The usual preoperative workup of breast malignancy consists of clinical breast examination and mammography with or without ultrasound. However, mammography and ultrasound fail to accurately assess tumour extent in as many as a third of patients eligible for breast conserving therapy. It is well established that magnetic resonance imaging is far superior to mammography (with and without ultrasound) for mapping the local extent of breast cancer. Experts advocate its use despite its high costs, high number of false positive findings, and lack of evidence from randomized prospective trials and, notably, fear of "overtreatment." This article discusses the current role of breast magnetic resonance imaging with its clinical advantages and applications.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".