Targeting the affective component of chronic pain: a case series of deep brain stimulation of the anterior cingulate cortex.
Bibliographic record
Abstract
BACKGROUND: Deep brain stimulation (DBS) has shown considerable promise for relieving nociceptive and neuropathic symptoms of refractory chronic pain. Nevertheless, for some patients, standard DBS for pain remains poorly efficacious. Pain is a multidimensional experience with an affective component: the unpleasantness. The anterior cingulate cortex (ACC) is a structure involved in this affective component, and targeting it may relieve patients' pain. OBJECTIVE: To describe the first case series of ACC DBS to relieve the affective component of chronic neuropathic pain. METHODS: Sixteen patients (13 male and 3 female patients) with neuropathic pain underwent bilateral ACC DBS. The mean age at surgery was 48.7 years (range, 33-63 years). Patient-reported outcome measures were collected before and after surgery using a Visual Analog Scale, SF-36 quality of life survey, McGill Pain Questionnaire, and EQ-5D (EQ-5D and EQ-5D Health State) questionnaires. RESULTS: Fifteen patients (93.3%) transitioned from externalized to fully internalized systems. Eleven patients had data to be analyzed with a mean follow-up of 13.2 months. Post-surgery, the Visual Analog Scale score dropped below 4 for 5 of the patients, with 1 patient free of pain. Highly significant improvement on the EQ-5D was observed (mean, +20.3%; range, +0%-+83%; P = .008). Moreover, statistically significant improvements were observed for the physical functioning and bodily pain domains of the SF-36 quality-of-life survey: mean, +64.7% (range, -8.9%-+276%; P = .015) and mean +39.0% (range, -33.8%-+159%; P = .050), respectively. CONCLUSION: Affective ACC DBS can relieve chronic neuropathic pain refractory to pharmacotherapy and restore quality of life.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".