Quantitative Assessment of Deep Brain Stimulation on Tremor in Multiple Sclerosis Disease
Bibliographic record
Abstract
Background and Purpose: Multiple sclerosis (MS) is a chronic disease that gives rise to a number of unpleasant symptoms that can be difficult to manage by conventional means. There are some aspects of MS-related disability which can be improved by using Deep Brain Stimulation (DBS); particularly movement disorders, such as tremor. The research aim is to study the effect of DBS on MS tremor with a quantitative approach. Methods: Raw tremor data was provided by the Surgical Center for Movement Disorders at the University of British Columbia, Canada. The hand tremor signal was collected on 8 MS subjects and 10 healthy individuals during finger-to-nose test (FNT) by using motion analysis system. Patients executed the FNT with DBS OFF and ON. Nonlinear and statistical features were extracted from tremor spectrum as tremor indexes. Tremor reduction by using DBS was determined by classifying mentioned features into three groups of healthy, DBS ON and DBS OFF. Results: Several dominant peaks were observed in power spectrum of the recorded signals which indicate MS tremor consists of several tremor subtypes due to different sites of demyelinating lesions affecting cerebellum and its output pathways. Results of frequency analysis revealed tremor spectrum dominant frequency in healthy group was significantly greater than for the patients with DBS ON and OFF (p<0.05). Additionally, classification results demonstrated that tremor of 6 patients out of 8 decreased significantly when their DBS was ON. Conclusion: Overall, in 75% of patients with MS who suffered from tremor, thalamic DBS significantly reduced their tremor. In addition, power spectrum dominant frequency has high potential for a quantitative and objective measure of MS tremor.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".