Values of pathological analysis of lost tissue fragments in the vacuum canister during a vacuum-assisted stereotactic biopsy of the breast
Bibliographic record
Abstract
OBJECTIVE: Determine values of pathological analysis of the canister content during a vacuum-assisted breast biopsy (VABB). METHODS: Approval was obtained from the ethical committee. Prospective radiological and pathological analyses of the canister content collected during 231 VABBs performed on 231 patients were carried out. χ(2) test was used to determine predictors on canister pathology. RESULTS: The canister pathology was reported separately in 212 cases. It showed only blood in 78/212 (37%) cases and benign (including high-risk lesions) and malignant results in, respectively, 113/212 (53%) and 21/212 (10%) cases. Respective specimen analysis was benign, including high-risk lesions in 162/212 cases (76%) and malignant in 50/212 (24%) cases. Microcalcifications were documented on canister X-ray in 70/231 (30%) cases. There was significant association between the canister and the specimen pathology (p<0.0001). In none of the cases was microcalcifications seen exclusively in the canister content or pathological upgrading found in the canister content compared with the specimen. CONCLUSION: Small tissue fragments and microcalcifications may be lost in the canister during a VABB. Nevertheless, our results did not show any significant value for systematic analysis of the canister content. ADVANCES IN KNOWLEDGE: There is no added diagnostic value to retrieval and analysis of tissue lost in the canister during a VABB.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".