Deep Brain Stimulation for Chronic Pain: Results of Two Multicenter Trials and a Structured Review
Bibliographic record
Abstract
OBJECTIVES: A U.S. Food and Drug Administration ruling required clinical trials to evaluate the safety and efficacy of deep brain stimulation devices, thereby limiting treatment to the investigational setting. INTRODUCTION: As an investigator in two clinical trials of deep brain stimulation, I sought to determine why pain remained an unapproved indication despite regulatory approval of the same device for tremor. METHODS: The results of two multicenter trials of deep brain stimulation for pain were analyzed, and the pertinent literature was reviewed using published guidelines for the evaluation of clinical trial reports. RESULTS: The first-generation Model 3380 lead trial enrolled 196 patients; the current Model 3387 trial enrolled 50 patients. Prospectively defined criteria for success included at least half of patients reporting >/=50% pain relief at 1 year. Manufacture of the Model 3380 lead was discontinued, and the 3387 trial closed early because of slow enrollment, high attrition, and low efficacy. When results were analyzed according to the study plan, neither trial was successful. Consequently, deep brain stimulation has not been approved for pain control by the U.S. Food and Drug Administration. CONCLUSIONS: Deep brain stimulation has not been shown to produce effective long-term pain relief. Future studies of motor cortex stimulation and similar therapies will require appropriate control groups and accepted methods of data collection and analysis to support claims that predictable and reliable analgesic effects are produced in humans.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".