Deep brain stimulation for the treatment of drug addiction
Bibliographic record
Abstract
Drug addiction represents a significant public health concern that has high rates of relapse despite optimal medical therapy and rehabilitation support. New therapies are needed, and deep brain stimulation (DBS) may be an effective treatment. The past 15 years have seen numerous animal DBS studies for addiction to various drugs of abuse, with most reporting decreases in drug-seeking behavior with stimulation. The most common target for stimulation has been the nucleus accumbens, a key structure in the mesolimbic reward pathway. In addiction, the mesolimbic reward pathway undergoes a series of neuroplastic changes. Chief among them is a relative hypofunctioning of the prefrontal cortex, which is thought to lead to the diminished impulse control that is characteristic of drug addiction. The prefrontal cortex, as well as other targets involved in drug addiction such as the lateral habenula, hypothalamus, insula, and subthalamic nucleus have also been stimulated in animals, with encouraging results. Although animal studies have largely shown promising results, current DBS studies for drug addiction primarily use stimulation during active drug use. More data are needed on the effect of DBS during withdrawal in preventing future relapse. The published human experience for DBS for drug addiction is currently limited to several promising case series or case reports that are not controlled. Further animal and human work is needed to determine what role DBS can play in the treatment of drug addiction.
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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 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".