Bibliographic record
Abstract
Focused ultrasound has been shown to be the only method that allows noninvasive thermal coagulation of tissues and recently this potential has been explored for image‐guided drug delivery. In this presentation, the advances in ultrasound phased array technology for energy delivery, exposure monitoring and control will be discussed. Experimental results from novel multi‐frequency transmit/receive arrays will be presented. In addition, the feasibility of fully electronically focused and steered high power arrays with many thousands of transducer elements will be discussed. Finally, some of the recent clinical and preclinical results for the treatment of brain disease will be reviewed. Learning Objectives: Introduce FUS therapy principles and modern techniques Discuss use of FUS for drug delivery Cover the technology required to deliver FUS and monitor therapy Present clinical examples of the uses of these techniques This research was supported by funding from The Canada Research Chair Program, Grants from CIHR and NIH (no. EB003268).; K. Hynynen, Canada Foundation for Innovation; Canadian Institutes of Health Research; Focused Ultrasound Surgery Foundation; Canada Research Chair Program; Natural Sciences and Engineering Research Council of Canada; Ontario Research Fund; National Institutes of Health; Canadian Cancer Society Research Institute; The Weston Brain Institute; Harmonic Medical; Focused Ultrasound Instruments
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".