Effect on biopsy technique of the breast imaging reporting and data system (BI-RADS) for nonpalpable mammographic abnormalities.
Bibliographic record
Abstract
OBJECTIVE: To determine if the breast imaging reporting and data system (BI-RADS) defines a group of patients with mammographic abnormalities in whom stereotactic core needle biopsy (SCNB) is appropriate. DESIGN: A blinded retrospective validation sample. SETTING: A university-affiliated hospital. PATIENTS: One hundred and nine consecutive patients who underwent fine-wire localization breast biopsy (FWLB) between Jan. 1, 1994, and June 1, 1999, with a known final pathological diagnosis. INTERVENTION: Blinded mammographic review and classification using the BI-RADS; review of corresponding pathological findings from FWLBs. OUTCOME MEASURES: Correlation of pathological findings with each BI-RADS category and analysis of the predictive value of clinical and radiologic features. RESULTS: BI-RADS findings were as follows: 0 malignant lesions in 10 category 3 cases, 18 malignant lesions (3 in situ, 15 invasive) in 68 category 4 cases and 24 malignant lesions (8 in situ and 16 invasive) in 31 category 5 cases. There was 1 malignant lesion in 22 category 4 cases in women younger than 50 years. CONCLUSIONS: SCNB should be applied to BI-RADS categories 3 and 4 (< 50 yr of age). FWLB should be reserved for category 4 (> 50 yr of age) and category 5 cases. This algorithm will reduce the morbidity and cost of breast biopsies in patients with nonpalpable mammographic abnormalities.
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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 | 0.014 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".