Clinical safety and efficacy of a fully automated robot for magnetic resonance imaging‐guided breast biopsy
Bibliographic record
Abstract
BACKGROUND: Magnetic resonance imaging (MRI)-guided biopsies are an accurate, but technically challenging, method for screening and diagnosis of breast lesions. This study assesses the safety and efficacy of an Image Guided Automated Robot (IGAR) in performing breast biopsies compared to manual procedures. METHODS: Safety was determined from adverse events (AEs) and device deficiencies. Efficacy was assessed using targeting accuracy, number of successful biopsies, pain and scar scores, patient discomfort, and radiologist-determined ease-of-use. RESULTS: All seven procedures in phase I were successfully and safely completed with no AEs and one device deficiency. The 23 IGAR biopsies in phase II outperformed the 18 manual biopsies in 1-week pain scores (p = 0.027), scarring at 1-week (p = 0.035), 1-month (p = 0.004), and components of comfort and ease-of-use. Phase II had seven and three AEs in the IGAR and manual groups, respectively (p = 0.317), with no serious AEs and nine device deficiencies. CONCLUSIONS: The IGAR system is safe and effective for breast biopsy procedures. The results from these trials indicate the IGAR system as a potentially viable alternative to manual breast biopsy procedures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".