Stereotaxic percutaneous core biopsy versus surgical biopsy of nonpalpable breast lesions using a standard mammographic table with an add-on device.
Bibliographic record
Abstract
OBJECTIVE: To determine the accuracy of using a regular mammographic table with an add-on device for biopsy of nonpalpable breast lesions in women in a community hospital setting. PATIENTS AND METHODS: During a 3-year period, 70 consenting women (39-80 years of age) with a nonpalpable mammographically suspicious lesion on routine screening mammography underwent 14-gauge automated percutaneous core biopsies, immediate needle localization and lumpectomy. The needle and surgical biopsy results were independently classified into 1 of 5 categories: cancer, fibroadenoma, fibrocystic change, normal or other. RESULTS: The procedure was well tolerated, and all core specimens yielded adequate tissue for pathologic evaluation. There were 3 episodes of vasovagal reaction. There was complete agreement in histologic findings in 64 cases (91%), including 22 of 24 cancers (92%). The overall agreement for categorizing lesions was 91% (kappa = 0.88), and there was 97% agreement (kappa = 0.94) for the classification of cancer versus benign lesion. CONCLUSION: The results are similar to those of studies performed with dedicated prone equipment. Stereotaxic core biopsies can be done safely and accurately in a community hospital setting with relatively inexpensive nondedicated mammographic equipment.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".