Bibliographic record
Abstract
Therapeutic ultrasound technology offers the ability to achieve non-invasive and non-ionizing treatments for soft-tissue disease throughout the body. The bio-effects in tissue caused by ultrasound energy can be thermal in nature (ranging from mild heating to tissue ablation) or can utilize the mechanical energy associated with acoustic waves. Ultrasound energy can be delivered from external devices and focused to a region of a few millimeters in the body. It can also be delivered from body cavities or from devices inserted directly into tissue. One challenge with designing therapeutic ultrasound systems is the need to consider wave propagation and distortion as energy passes through tissues. Consequently, therapeutic ultrasound systems are usually designed to target specific anatomic sites to optimize the delivery of ultrasound to these locations. In this session a number of advanced therapeutic ultrasound delivery systems will be described for the treatment of breast, brain, and prostate tissue. In addition the talks will describe both thermal and non-thermal applications of ultrasound energy. Specific topics addressed in this symposium include: (1) Advances in Focused Ultrasound Brain Therapies, (2) Breast MR guided Focused Ultrasound Hardware Design and Treatment Strategies, (3) The Role of Pulsed Focused Ultrasound in Cancer Therapy, and (4) Sonication and Feedback Control Strategies for MR guided Hyperthermia with the Insightec Prostate Array. Learning Objectives: 1. Understand the technical issues important in the design of therapeutic ultrasound systems 2. Learn about therapeutic ultrasound systems under development for brain, breast and prostate applications 3. Learn about different treatment approaches with therapeutic ultrasound including tissue ablation, hyperthermia, and drug delivery Lili Chen: Funding: FUS Foundation, DOD (PC073127,BC102806) Allison Payne: Funding: NIH (R01EB013433,R01CA178727) Vasant Salgaonkar: Funding: FUS Foundation, NIH (R01CA122276, R01CA111981)
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.061 | 0.031 |
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".