Randomized clinical trial of deep brain stimulation for poststroke pain
Bibliographic record
Abstract
OBJECTIVE: The experience with deep brain stimulation (DBS) for pain is largely based on uncontrolled studies targeting the somatosensory pathways, with mixed results. We hypothesized that targeting limbic neural pathways would modulate the affective sphere of pain and alleviate suffering. METHODS: We conducted a prospective, double-blinded, randomized, placebo-controlled, crossover study of DBS targeting the ventral striatum/anterior limb of the internal capsule (VS/ALIC) in 10 patients with poststroke pain syndrome. One month after bilateral DBS, patients were randomized to active DBS or sham for 3 months, followed by crossover for another 3-month period. The primary endpoint was a ≥50% improvement on the Pain Disability Index in 50% of patients with active DBS compared to sham. This 6-month blinded phase was followed by an 18-month open stimulation phase. RESULTS: Nine participants completed randomization. Although this trial was negative for its primary and secondary endpoints, we did observe significant differences in multiple outcome measures related to the affective sphere of pain (eg, Montgomery-Åsberg Depression Rating Scale, Beck Depression Inventory, Affective Pain Rating Index of the Short-Form McGill Pain Questionnaire). Fourteen serious adverse events were recorded and resolved. INTERPRETATION: VS/ALIC DBS to modulate the affective sphere of pain represents a paradigm shift in chronic pain management. Although this exploratory study was negative for its primary endpoint, VS/ALIC DBS demonstrated an acceptable safety profile and statistically significant improvements on multiple outcome measures related to the affective sphere of pain. Therefore, we believe these results justify further work on neuromodulation therapies targeting the affective sphere of pain. Ann Neurol 2017;81:653-663.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".