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Record W4402473507 · doi:10.1126/science.adp7206

Exploiting the mechanical effects of ultrasound for noninvasive therapy

2024· review· en· W4402473507 on OpenAlexaff
Meaghan A. O’Reilly

Bibliographic record

VenueScience · 2024
Typereview
Languageen
FieldEngineering
TopicUltrasound and Hyperthermia Applications
Canadian institutionsUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsNeuromodulationUltrasoundFocused ultrasoundMedicineThermal ablationTherapeutic ultrasoundNeuroscienceAblationRadiologyCentral nervous systemBiologyInternal medicine

Abstract

fetched live from OpenAlex

Focused ultrasound is a platform technology capable of eliciting a wide range of biological responses with high spatial precision deep within the body. Although focused ultrasound is already in clinical use for focal thermal ablation of tissue, there has been a recent growth in development and translation of ultrasound-mediated nonthermal therapies. These approaches exploit the physical forces of ultrasound to produce a range of biological responses dependent on exposure conditions. This review discusses recent advances in four application areas that have seen particular growth and have immense clinical potential: brain drug delivery, neuromodulation, focal tissue destruction, and endogenous immune system activation. Owing to the maturation of transcranial ultrasound technology, the brain is a major target organ; however, clinical indications outside the brain are also discussed.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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.

Opus teacher head0.035
GPT teacher head0.314
Teacher spread0.279 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations121
Published2024
Admission routes1
Has abstractyes

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