An Ultrasonically Actuated Fine Needle Enhances Biopsy Sample Yield
Bibliographic record
Abstract
Fine-needle aspiration biopsy (FNAB) is a well-known procedure employed in the diagnostics of various tissue pathologies. Despite the common use of fine-needles in biopsy, substantial limitations related to the yield insufficiency of the biopsy sample still remain. In this study, we employed a custom-made ultrasonic device operating at 33 kHz to induce flexural standing waves in a standard 21 G medical needle. This was followed by obtaining tissue samples with the method we call ultrasound-enhanced FNAB (USeFNAB) in different bovine tissues ex vivo. We demonstrated that the yield of USeFNAB was on average up to 3-6 x compared to the yield obtained with the FNAB approach. Histologically relevant structures were detected under microscopy in samples obtained with both techniques. USeFNAB represents a promising candidate for resolving the issue of sample insufficiency, which largely limits the reliability of the FNAB approach.
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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 | 0.001 |
| 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 | 0.000 | 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".