Sci‐Fri AM: YIS‐05: A new guidance device for lateral‐approach stereotactic breast biopsy
Bibliographic record
Abstract
Stereotactic breast biopsy (SBB) is the gold standard for noninvasive breast cancer diagnosis. Current systems rely on one of two methods for needle insertion: a top-approach (from above the breast compression plate) or a lateral-approach (parallel to the compression plate). While the top-approach is more commonly used, it is not feasible in patients with thin breasts (less than 2.5 cm thickness after compression), or with superficial lesions. We present a novel design of lateral guidance support for SBB, which addresses these limitations of the top-approach, and provides improvements over existing lateral support hardware. This device incorporates spherical linkages to allow two degrees of rotational freedom in the needle trajectory for increased targeting flexibility, as well as an adjustable rigid needle support to minimize needle deflection within the tissue. Needle placement error in SBB experiments is compared using both the new lateral guidance device and a commercial lateral guidance device in agar phantoms. The effect of elevation angle on needle placement accuracy using the new lateral guidance device is also assessed. Finally, a biopsy accuracy experiment is presented using a certified SBB phantom to compare the new design and the commercial lateral guidance device. In these experiments, SBB performed using the new lateral guidance device results in improved needle placement error and biopsy accuracy, while increasing targeting flexibility and maintaining procedural workflow.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".