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Record W4364377550 · doi:10.1093/brain/awad123

The return of the lesion for localization and therapy

2023· article· en· W4364377550 on OpenAlexaff
Juho Joutsa, Nir Lipsman, Andreas Horn, G. Rees Cosgrove, Michael Fox

Bibliographic record

VenueBrain · 2023
Typearticle
Languageen
FieldMedicine
TopicNeurological disorders and treatments
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
FundersNational Institute of Neurological Disorders and StrokeNational Institute of Mental HealthNational Institutes of HealthTurun YliopistoDeutsches Zentrum für Luft- und RaumfahrtNational Institute on AgingNew Venture FundTurun Yliopistollinen KeskussairaalaAlkoholitutkimussäätiöSigrid Juséliuksen SäätiöSuomen Lääketieteen SäätiöEU Joint Programme – Neurodegenerative Disease ResearchDeutsche Forschungsgemeinschaft
KeywordsLesionDeep brain stimulationNeuroimagingMedicineNeurosciencePathologicalTherapeutic approachPsychologyPathologyDiseaseParkinson's disease

Abstract

fetched live from OpenAlex

Historically, pathological brain lesions provided the foundation for localization of symptoms and therapeutic lesions were used as a treatment for brain diseases. New medications, functional neuroimaging and deep brain stimulation have led to a decline in lesions in the past few decades. However, recent advances have improved our ability to localize lesion-induced symptoms, including localization to brain circuits rather than individual brain regions. Improved localization can lead to more precise treatment targets, which may mitigate traditional advantages of deep brain stimulation over lesions such as reversibility and tunability. New tools for creating therapeutic brain lesions such as high intensity focused ultrasound allow for lesions to be placed without a skin incision and are already in clinical use for tremor. Although there are limitations, and caution is warranted, improvements in lesion-based localization are refining our therapeutic targets and improved technology is providing new ways to create therapeutic lesions, which together may facilitate the return of the lesion.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.603
Threshold uncertainty score0.052

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.046
GPT teacher head0.313
Teacher spread0.267 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations36
Published2023
Admission routes1
Has abstractyes

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