Ultrasonographic findings 6 months after 11-gauge vacuum-assisted large-core breast biopsy.
Bibliographic record
Abstract
OBJECTIVE: To assess the ultrasonographic features of post-biopsy change 6 months after 11-gauge vacuum-assisted large-core breast biopsy of pathologically proven benign lesions. Using the literature as a reference, we hypothesized that large-core breast biopsy would result in tissue changes that may mimic malignancy and may be more apparent on ultrasonography than on mammography. METHODS: Two radiologists whose subspecialty is breast imaging retrospectively reviewed the pre-biopsy and 6-month follow-up sonograms of 24 patients with pathologically proven benign lesions. The images were assessed for the number and type of ultrasonographic features. A Breast Imaging Reporting and Data System (BI-RADS) category was assigned to each lesion before biopsy and at 6-month follow-up. The composition of breast tissue surrounding the lesion was assessed as fatty, mixed fibroglandular or dense. RESULTS: The frequency of ultrasonographic changes at 6 months after 11-gauge vacuum-assisted large-core breast biopsy was more frequent than the rate of post-biopsy change previously reported to occur mammographically. The nature of these changes may mimic malignancy in some cases. CONCLUSION: The ultrasonographic appearance of the breast after large-core breast biopsy may mimic malignancy and is, therefore, a potential pitfall when interpreting a post-biopsy sonogram.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".