Deep brain stimulation in the ventrolateral thalamus/subthalamic area in dystonia with head tremor
Bibliographic record
Abstract
BACKGROUND: Pallidal deep brain stimulation (GPi-DBS) effectively ameliorates idiopathic dystonia, although approximately 15% of patients respond insufficiently. Although various thalamic and subthalamic targets have been suggested for dystonic tremor, no systematic studies have been published on thalamic DBS in dystonic tremor. We assessed the effect of thalamic/subthalamic area DBS (Th-DBS) on dystonic head tremor and dystonia in a single-blind design. METHODS: Dystonic head tremor and dystonia before and 3 months after surgery were quantified via blinded video-ratings using the Fahn-Tolosa-Marin-Tremor-Scale and the Burke-Fahn-Marsden-Dystonia-Rating-Scale in seven patients with idiopathic cervical or segmental dystonia, dystonic head tremor, and bilateral Th-DBS. Pain, side effects, adverse events, and stimulation parameters were assessed. RESULTS: Th-DBS improved dystonic tremor and dystonia (P < 0.05; 57.1% and 70.4%, respectively). Head tremor amplitude and pain were also improved (P < 0.05; 77.5% and 90.0%, respectively). Side effects included dysarthria, gait disturbance, slowness of movement, and weight gain. CONCLUSION: Dystonic head tremor and dystonia can be improved with Th-DBS.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".