Breast Magnetic Resonance Imaging: Are Those Who Need It Getting It?
Bibliographic record
Abstract
BACKGROUND: Indications for breast magnetic resonance imaging (mri), a very sensitive but less-specific tool for breast investigation, remain controversial, and accessibility is limited. The purposes of our study were to determine the proportion of breast mri exams performed for various clinical indications, to assess the wait times for breast mri, and to create a list of evidence-based indications for breast mri. METHODS: The indications for breast mri exams performed in September 2013 at our academic centre were audited. A multidisciplinary meeting held in May 2014 established a list of evidence-based indications for breast mri, after which, in September 2014 and 2015, breast mri exams were re-audited for clinical indications, and pending requests were calculated. RESULTS: In September 2013, surveillance of women with a prior diagnosis of breast cancer represented 21% of breast mri exams (24 of 113), with preoperative staging representing 18% of exams (20 of 113) and high-risk screening representing 12% (13 of 113). Of pending mri requests, 230 were within the recommended delay period, and 457 exceeded the recommended delay. After elaboration of evidence-based guidelines, repeat audits in September 2014 and September 2015 showed that mri performed for women with a prior breast cancer diagnosis represented 23% (33 of 141) and 7% (10 of 143) of exams respectively, with preoperative staging having declined to 9% (13 of 141) and 11% (16 of 143) of exams, and high-risk screening having increased to 36% (51 of 141) and 46% (66 of 143) of exams. Overall, wait times were improved for all breast mri indications. CONCLUSIONS: Through multidisciplinary discussion, we actualized a list of breast mri indications, prioritized requests more adequately, and improved wait times.
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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 | 0.004 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".