Fixing a Shaky Video to Remotely Program Deep Brain Stimulation
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".