Deep brain stimulation of the dorsal anterior cingulate cortex for the treatment of chronic neuropathic pain
Bibliographic record
Abstract
Chronic neuropathic pain is estimated to affect 3%-4.5% of the worldwide population. It is associated with significant loss of productive time, withdrawal from the workforce, development of mood disorders such as depression and anxiety, and disruption of family and social life. Current medical therapeutics often fail to adequately treat chronic neuropathic pain. Deep brain stimulation (DBS) targeting subcortical structures such as the periaqueductal gray, the ventral posterior lateral and medial thalamic nuclei, and the internal capsule has been investigated for the relief of refractory neuropathic pain over the past 3 decades. Recent work has identified the dorsal anterior cingulate cortex (dACC) as a new potential neuromodulation target given its central role in cognitive and affective processing. In this review, the authors briefly discuss the history of DBS for chronic neuropathic pain in the United States and present evidence supporting dACC DBS for this indication. They review existent literature on dACC DBS and summarize important findings from imaging and neurophysiological studies supporting a central role for the dACC in the processing of chronic neuropathic pain. The available neurophysiological and empirical clinical evidence suggests that dACC DBS is a viable therapeutic option for the treatment of chronic neuropathic pain and warrants further investigation.
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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 | 0.000 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".