Surgical Attitudes toward Preoperative Breast Magnetic Resonance Imaging in Women with Early-Stage Breast Cancer
Bibliographic record
Abstract
Background: Preoperative breast magnetic resonance imaging (mri) is commonly requested by surgeons in the initial workup of women with breast cancer; however, its use is controversial. We performed a survey of breast cancer surgeons across Canada to investigate current knowledge about, attitudes to, and self-reported use of preoperative breast mri in a publicly funded health care system in light of the limited evidence to support it. Methods: All identified general surgeons in Canada were mailed a survey instrument designed to probe current practice and knowledge of published trials. Results: Of 403 responding surgeons, 233 (58%) indicated that they performed breast cancer surgery. Of those 233, 218 (94%) had access to breast mri and completed the entire survey. Overall, 54.6% of responding surgeons felt that breast mri was useful in surgical planning, and more than half (58.3%) indicated that their frequency of use was likely to increase over the next 5 years. Surgeons found preoperative mri most useful in detecting mammographically occult disease (71.5% of respondents) and in planning for breast-conserving surgery (57.3%). The main limitations reported were timely access to mri (51%) and false positives (36.7%). Responses suggest a knowledge gap in awareness of published trials in breast mri. Conclusions: Our study found that, in early-stage breast cancer, self-reported use of mri by breast cancer surgeons in Canada varied widely. Reported indications did not align with published data, and significant gaps in self-reported knowledge of the data were evident. Our results would support the development and dissemination of guidelines to optimize use of mri.
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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 | 0.002 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".