New neurosurgical approaches for tremor and Parkinson's disease
Bibliographic record
Abstract
PURPOSE OF REVIEW: The objective of this review is to gather the newest advances in the surgical treatment of tremor and Parkinson's disease. We will briefly discuss the potential applications of the new technologies of deep brain stimulation (DBS), and we will focus on MRI-guided focused ultrasound (MRgFUS). RECENT FINDINGS: Novel DBS devices are being progressively adopted, particularly electrodes allowing a longer stimulating surface (suitable for multiple targets stimulation) and current radial steering (to minimize side effects of stimulation). New implantable pulse generators are also able to record neurons and are generating enough knowledge to advance the implementation of adaptive (closed-loop) DBS.Over the last years, 'minimally-invasive' neurosurgical approaches for the treatment of movement disorders have been developed: gamma knife radiosurgery and MRgFUS. Uncontrolled and recent controlled studies have shown the benefits of MRgFUS targeting the thalamus and pallidus for the treatment of tremor and Parkinson's disease. SUMMARY: The initial clinical data are certainly promising and have expanded the current portfolio of neurosurgical treatments of movement disorders. Many issues are yet to be addressed, particularly safety of MRgFUS-and how these new treatments compare with the existing ones.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 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) | 0.005 | 0.002 |
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".