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Record W3023434658 · doi:10.1016/j.brs.2020.04.020

Treating the brain at the speed of sound

2020· editorial· en· W3023434658 on OpenAlexaff
Roland Beisteiner, Andrés M. Lozano

Bibliographic record

VenueBrain stimulation · 2020
Typeeditorial
Languageen
FieldEngineering
TopicUltrasound and Hyperthermia Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineFocused ultrasoundIntensive care medicinePublic healthUltrasoundNeurosciencePsychologyPathologyRadiology

Abstract

fetched live from OpenAlex

One of the most important public issues in our rapidly ageing society are brain disorders and neurodegenerative diseases. Effective therapies are limited and therefore costs for public health systems rapidly increase in this sector. However, recently the first clinical evidence for a new class of therapies has emerged - ultrasound for the brain. With the first clinical data on ultrasound brain activation just now published, three fascinating options are available to revolutionize non-invasive brain therapy. All three ultrasound therapies currently receive widespread attention from patients and doctors.

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)0.001
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: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.225
Threshold uncertainty score0.526

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.013
GPT teacher head0.252
Teacher spread0.239 · 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
GenreEditorial

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

Citations5
Published2020
Admission routes1
Has abstractyes

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