MétaCan
Menu
Back to cohort
Record W4388378417 · doi:10.1016/j.brs.2023.10.014

Rethinking NBM DBS: Intermittent stimulation improves sustained attention in Parkinson's disease

2023· letter· en· W4388378417 on OpenAlexafffundabout
Sanskriti Sasikumar, Mélanie Cohn, Ariana Youm, Katherine Duncan, Alexandra Boogers, Antonio P. Strafella, David T. Blake, Alfonso Fasano

Bibliographic record

VenueBrain stimulation · 2023
Typeletter
Languageen
FieldMedicine
TopicNeurological disorders and treatments
Canadian institutionsCentre for Addiction and Mental HealthOntario Brain InstituteToronto Western HospitalUniversity of Toronto
FundersCanadian Institutes of Health ResearchUniversity of TorontoBoston Scientific Corporation
KeywordsNucleus basalisDeep brain stimulationNeuroscienceParkinson's diseaseStimulationCholinergicGlobus pallidusMedicineCognitive declineDiseasePsychologyCholinergic neuronDementiaBasal gangliaInternal medicineCentral nervous system

Abstract

fetched live from OpenAlex

Degeneration of the Nucleus Basalis of Meynert (NBM) has been implicated in the cognitive decline in Parkinson's disease (PD) [1] and has therefore been a target of interest for deep brain stimulation (DBS). We have previously published on the feasibility and safety of NBM and globus pallidus interna stimulation in PD with advanced cognitive impairment [2]. The NBM was programmed to performance on sustained attention as it is a sensitive marker of cholinergic activity [3] and is crucial to several activities of daily living [4].

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: Commentary · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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)0.0020.000

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 head0.034
GPT teacher head0.298
Teacher spread0.264 · 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
GenreCommentary

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
Published2023
Admission routes3
Has abstractyes

Explore more

Same venueBrain stimulationSame topicNeurological disorders and treatmentsFrench-language works237,207