Bibliographic record
Abstract
PURPOSE OF REVIEW: The purpose of this review was to review the recent and future developments of deep brain stimulation (DBS) for movement disorders. RECENT FINDINGS: In the last 2 years, we have gained a better understanding of established indications, particularly with respect to the debate on whether subthalamus or globus pallidus pars interna should be the target of choice for Parkinson's disease. In addition, the role of DBS for dystonia has been further defined in terms of patients' selection and outcome of surgery. Other established (e.g. essential tremor) and novel indications (e.g. Tourette syndrome) have been addressed. Along with the evolving knowledge of the clinical aspects of DBS, technological advances are also shaping the present and the future of DBS. New implantable pulse generators (e.g. allowing storage of electrophysiological data and eventual adaptive stimulation) as well as new electrode configurations are now available. Furthermore, high-resolution structural imaging, including high-field MRI and diffusion tensor tractography, will facilitate both the planning of DBS procedures, and the optimization of postoperative outcomes by aiding stimulation programming. SUMMARY: The recent successes of DBS along the clinical and technological directions are changing the current practice of neuromodulation and, more importantly, will also drive future developments of this fascinating treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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) | 0.009 | 0.005 |
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".