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Record W2946993208 · doi:10.1002/mds.27722

Focused ultrasound opening of the blood–brain barrier for treatment of Parkinson's disease

2019· review· en· W2946993208 on OpenAlexaff
Peter A. LeWitt, Nir Lipsman, Jeffrey H. Kordower

Bibliographic record

VenueMovement Disorders · 2019
Typereview
Languageen
FieldEngineering
TopicUltrasound and Hyperthermia Applications
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsFocused ultrasoundMedicineParkinson's diseaseUltrasound energyDiseaseUltrasoundEssential tremorBlood–brain barrierNeurosciencePathologyPhysical medicine and rehabilitationCentral nervous systemPsychologyInternal medicineRadiology

Abstract

fetched live from OpenAlex

The expanding landscape of options for Parkinson's disease (PD) therapeutics calls for novel ways to improve delivery of treatments to counteract neurodegeneration or enhance symptomatic control. This unmet need is particularly relevant for opportunities in gene therapy, which, in recent PD clinical trials, has required invasive neurosurgical approaches into the CNS. One of the promising techniques to bring new therapies into the brain for PD therapeutics involves an evolving technology, focused ultrasound. Focused ultrasound has been used to alleviate tremor by thermal ablation with high-energy sonication. Using similar equipment but much lower sonication energy, focused ultrasound assisted with micro-bubbles can temporarily open the blood-brain barrier at specific brain targets to facilitate real-time magnetic resonance-guided delivery of therapeutic agents. To explore the current status and future of focused ultrasound in transvascular therapeutics for PD, a November 2018 workshop reviewed its accomplishments and challenges. This report summarizes key points of discussion and provides further background to the promising roles focused ultrasound offers. © 2019 International Parkinson and Movement Disorder Society.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.993
Threshold uncertainty score0.946

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.025
GPT teacher head0.265
Teacher spread0.240 · 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 teacher head, 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

Citations45
Published2019
Admission routes1
Has abstractyes

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