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Record W4412693462 · doi:10.5334/tohm.963

Fixing a Shaky Video to Remotely Program Deep Brain Stimulation

2025· article· en· W4412693462 on OpenAlexafffund
Maria Belen Justich, Alexandra Boogers, Andrés M. Lozano, Alfonso Fasano

Bibliographic record

VenueTremor and Other Hyperkinetic Movements · 2025
Typearticle
Languageen
FieldMedicine
TopicNeurological disorders and treatments
Canadian institutionsOntario Brain InstituteUniversity of TorontoToronto Western Hospital
FundersCanadian Institutes of Health ResearchIpsenUniversity of TorontoBoston Scientific Corporation
KeywordsDeep brain stimulationComputer scienceMedicine

Abstract

fetched live from OpenAlex

Deep brain stimulation (DBS) is an increasingly utilized therapy for treating refractory tremor in Parkinson's disease (PD). Remote care can improve patient access to specialized DBS clinics. Here, we present a novel strategy to assess tremor remotely during DBS programming. We report the case of a 65-year-old female diagnosed with PD who showed only partial responsiveness to levodopa. She underwent bilateral subthalamic nucleus DBS surgery and was implanted with an Infinity™ implantable pulse generator with directional leads (Abbott, Chicago, IL, USA). Given that she resided 1,500 km from our center, device programming was performed remotely using the Neurosphere™ Virtual Clinic platform. The patient was instructed to hold her controller in a fixed position until her resting tremor re-emerged, which was visibly evident through a shaking video frame. Stimulation parameters were then optimized until the video frame became still. She reported sustained benefit during follow-up. We propose that this alternative method for remotely assessing upper limb tremor may offer advantages for healthcare providers, allowing them to base stimulation adjustments on visually observable tremor severity.

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 score0.765
Threshold uncertainty score0.486

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 head0.024
GPT teacher head0.318
Teacher spread0.294 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

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