Training module to teach ultrasound-guided breast biopsy skills to residents improves accuracy.
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effectiveness of a training module in teaching residents the skills necessary to perform accurate and safe ultrasound-guided breast biopsies (USGBB). METHODS: Twelve residents with no USGBB experience, but variable ultrasound (US) experience, were randomly assigned to 2 groups; 1 group participated in a training module, and the other received no training. Each resident then attempted 30 core biopsies of "lesions" implanted in breast phantoms. Successful biopsies extracted some "lesion" material. "Chest wall" hits were also counted. RESULTS: The trained residents had significantly fewer "chest wall" hits than the untrained group (p < 0.002), but there was no significant difference in the number of successful biopsies (73% v. 43%, p = 0.09). The subgroup of residents who were USGBB trained but inexperienced in US (n = 4) achieved more successful biopsies (p < 0.05) and fewer "chest wall" hits (p < 0.01) than their matched untrained cohort (n = 3). The trained US-experienced subgroup (n = 2) had fewer "chest wall" hits than the matched untrained subgroup (n = 3; p < 0.05) but similar biopsy success rates. Untrained US-experienced residents (n = 3) had more successful biopsies than untrained US-inexperienced residents (n = 3; p < 0.001) and similar "chest wall" hits. CONCLUSION: Residents with training perform USGBBs more safely, and training significantly improves accuracy of USGBB in residents with no US experience. US experience improves biopsy success rates but does not affect safety levels of residents with no USGBB training.
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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".