Predictors of Deep Brain Stimulation Outcome in Tremor Patients (P4.298)
Bibliographic record
Abstract
Objective: We sought to identify clinical or surgical characteristics predicting response to deep brain stimulation (DBS) for tremor, hypothesizing that medication and/or alcohol responsiveness might correlate with good outcome. Background: Tremor is a common and disabling feature of many disorders. Medical therapy for tremor is often ineffective, and clinical factors predicting response to DBS for tremor are poorly characterized. We examined clinical and surgical characteristics to identify patients obtaining the most benefit from DBS. Methods: We performed a retrospective chart review of all patients who underwent nucleus ventralis intermedius (Vim) DBS for tremor from 1995-2013, with 52 patients meeting inclusion criteria. We collected demographic data, clinical data including alcohol and medication responsiveness, surgical data including active electrode location and stimulation parameters, and tremor rating scales preoperatively and at 1-year and last recorded follow-up. Results: All groups (Parkinson’s disease [PD], essential tremor [ET], and cerebellar tremor [CT]) showed a reduction in contralateral tremor scores following Vim DBS at 1-year and last follow-up. Underlying diagnosis was the strongest predictor of long-term benefit, with no failures in the PD group, and significantly higher self-reported loss of benefit in CT than ET (P=0.002). We found that more posterior electrode placement was significantly associated with failure of benefit at 1 year (p=0.011). No other predictors were detected. Conclusions: Clinical factors that may help identify good candidates for DBS for tremor remain unclear. Overall, patients with an underlying diagnosis of PD had the best response. Electrode placement within the Vim nucleus of the thalamus, possibly closer to the ventralis oralis posterior, may increase the likelihood of sustained benefit from DBS for 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".