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Record W2944875226 · doi:10.17925/usn.2019.15.1.18

Imaging, Technology, and Parkinson’s Disease

2019· article· en· W2944875226 on OpenAlexaff
Nora Vanegas

Bibliographic record

VenuetouchREVIEWS in Neurology · 2019
Typearticle
Languageen
FieldMedicine
TopicNeurological disorders and treatments
Canadian institutionsCentre for Movement Disorders
Fundersnot available
KeywordsDeep brain stimulationMedicineMovement disordersDystoniaEssential tremorNeuromodulationParkinson's diseaseNeuroimagingTranslational researchNeurosurgeryNeurologyClinical neuroscienceNeurosciencePhysical medicine and rehabilitationDiseasePsychologyPsychiatryPathologyInternal medicine

Abstract

fetched live from OpenAlex

<italic><bold>Nora Vanegas</bold> Dr Vanegas is a neurologist who specializes in deep brain stimulation (DBS) and the treatment of movement disorders including Parkinson’s disease, dystonia, and essential tremor. Dr Vanegas completed her combined clinical-research fellowship at the National Institutes of Health (NIH) under the mentorship of Dr Mark Hallett. Her training had a special focus on neuroimaging and neuromodulation. She transitioned to being an Assistant Professor of Neurology in Columbia University in 2016, and is now an established local expert in neuromodulation for movement disorders. Dr Vanegas is also a clinical investigator whose research involves clinical and translational areas of movement disorders, specifically the use of brain imaging for the understanding of DBS and the physiology of the basal ganglia. As part of multi-disciplinary research activities, Dr Vanegas has developed strong collaborations for various projects with the departments of Biomedical Engineering, Speech Pathology, Neurosurgery and Psychiatry at Columbia University. Such collaborative research activities include the use of instrumented assessments to measure gait characteristics in patients with Parkinson’s disease, the benefits of various airway protection interventions in patients with Parkinson’s disease who aspirate with food and the activity of brain neurons during decision making tasks.</italic>

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.248
Threshold uncertainty score0.450

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 head0.012
GPT teacher head0.272
Teacher spread0.260 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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