Translational aspects of deep brain stimulation for chronic pain
Bibliographic record
Abstract
The use of deep brain stimulation (DBS) for the treatment of chronic pain was one of the first applications of this technique in functional neurosurgery. Established brain targets in the clinic include the periaqueductal (PAG)/periventricular gray matter (PVG) and sensory thalamic nuclei. More recently, the anterior cingulum (ACC) and the ventral striatum/anterior limb of the internal capsule (VS/ALIC) have been investigated for the treatment of emotional components of pain. In the clinic, most studies showed a response in 20%-70% of patients. In various applications of DBS, animal models either provided the rationale for the development of clinical trials or were utilized as a tool to study potential mechanisms of stimulation responses. Despite the complex nature of pain and the fact that animal models cannot reliably reflect the subjective nature of this condition, multiple preparations have emerged over the years. Overall, DBS was shown to produce an antinociceptive effect in rodents when delivered to targets known to induce analgesic effects in humans, suggesting a good predictive validity. Compared to the relatively high number of clinical trials in the field, however, the number of animal studies has been somewhat limited. Additional investigation using modern neuroscience techniques could unravel the mechanisms and neurocircuitry involved in the analgesic effects of DBS and help to optimize this therapy.
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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) | 0.001 | 0.001 |
| 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 | 0.001 | 0.002 |
| 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".