Deep brain stimulation: new programming algorithms and teleprogramming
Bibliographic record
Abstract
INTRODUCTION: Thanks to a variety of factors, the field of neuromodulation has evolved significantly over the past decade. Developments include new indications and innovations of hardware, software, and stimulation techniques leading to an expansion in scope and role of these techniques as powerful therapies. They also imply the realization that practical application involves new nuances that make patient selection, surgical technique, and the programming process even more complex, requiring continuous education and an organized structured approach. AREAS COVERED: In this review, the authors explore the developments in deep brain stimulation (DBS) technology, including electrodes, implantable pulse generators, contact configurations (i.e. directional leads and independent current control), remote programming, and sensing using local field potentials. EXPERT OPINION: The innovations in the field of DBS discussed in this review potentially provide increased effectiveness and flexibility not only to improve therapeutic response but also to address troubleshooting challenges seen in clinical practice. Directional leads and shorter pulse widths may broaden the therapeutic window of stimulation, avoiding current spread to structures that might trigger stimulation-related side effects. Similarly, independent control of current to individual contacts allows for the shaping of the electric field. Finally, sensing and remote programming represent important developments for more effective and individualized patient care.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".