Advances in Technical Aspects of Deep Brain Stimulation Surgery
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Deep brain stimulation has become an established technology for the treatment of patients with a wide variety of conditions, including movement disorders, psychiatric disorders, epilepsy, and pain. Surgery for implantation of DBS devices has enhanced our understanding of human physiology, which in turn has led to advances in DBS technology. Our group has previously published on these advances, proposed future developments, and examined evolving indications for DBS. SUMMARY: The crucial roles of structural MR imaging pre-, intra-, and post-DBS procedure in target visualization and confirmation of targeting are described, with discussion of new MR sequences and higher field strength MRI enabling direct visualization of brain targets. The incorporation of functional and connectivity imaging in procedural workup and their contribution to anatomical modelling is reviewed. Various tools for targeting and implanting electrodes, including frame-based, frameless, and robot-assisted, are surveyed, and their pros and cons are described. Updates on brain atlases and various software used for planning target coordinates and trajectories are presented. The pros and cons of asleep versus awake surgery are discussed. The role and value of microelectrode recording and local field potentials are described, as well as the role of intraoperative stimulation. Technical aspects of novel electrode designs and implantable pulse generators are presented and compared.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 it