Deep brain stimulation for Parkinsonʼs disease and other movement disorders
Bibliographic record
Abstract
PURPOSE OF REVIEW: Deep brain stimulation (DBS) is now widely used in the treatment of Parkinson's disease, tremor, and dystonia. This review examines recent developments in the application of DBS to the management of movement disorders. RECENT FINDINGS: In Parkinson's disease, recent work has demonstrated that early DBS may have a significant benefit on quality of life and motor symptoms while permitting a decrease in levodopa equivalent dosage. Thalamic DBS continues to be a well established target for the treatment of tremor, although recent work suggests that alternative targets such as the posterior subthalamic area may be similarly efficacious. The treatment of primary dystonia with DBS has been established in multiple recent trials, demonstrating prolonged symptomatic benefit. SUMMARY: DBS is now an established symptomatic treatment modality for Parkinson's disease and other movement disorders. Future work will undoubtedly involve establishing new indications and targets in the treatment of movement disorders with further refinements to existing technology. Ultimately, these methods combined with biologically based therapies may catalyze a shift from symptomatic treatment to actually modifying the natural history of neurodegenerative diseases such as Parkinson's disease.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".