Deep Brain Stimulation in Clinical Practice and in Animal Models
Bibliographic record
Abstract
Deep brain stimulation (DBS) has dramatically changed the landscape of neurosurgery. Overall, the technique consists of the delivery of current into the brain parenchyma through implanted electrodes (Figure 1). It is estimated that 60,000 patients worldwide have received DBS, with the rate of accrual currently approaching 8,000 to 10,000 new patients a year. Although electrical stimulation has been used for more than 50 years to treat psychiatric disorders and pain, the technique as conducted today reemerged some 25 years ago, in the field of movement disorders. The striking clinical effects of DBS in these conditions and the similarities in outcome between stimulation and lesions soon prompted the investigation of the technique for various diseases previously treated by functional neurosurgeons. Equally important for the development of the therapy were imaging and electrophysiological studies. Because DBS modulates local neuronal activity and influences regions at a distance from the stimulated site, dysfunctional anatomic circuits and structures have been regarded as potential targets. Animal research has provided the rationale for the use of DBS in some applications of the therapy, although most experimental studies have been conducted to explore potential mechanisms for the effects of stimulation. Clinical Pharmacology & Therapeutics (2010) 88 4, 559–562. doi:10.1038/clpt.2010.133
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".