Deep brain stimulation for intractable neuropathic facial pain
Bibliographic record
Abstract
OBJECTIVE Deep brain stimulation (DBS) is a well-established, evidence-based therapy with FDA approval for Parkinson's disease and essential tremor. Despite the early successful use of DBS to target the sensory thalamus for intractable facial pain, subsequent studies pursuing various chronic pain syndromes reported variable efficacy, keeping DBS for pain as an investigational and "off-label" use. The authors report promising results for a contemporary series of patients with intractable facial pain who were treated with DBS. METHODS Pain outcomes for 7 consecutive patients with unilateral, intractable facial pain undergoing DBS of the ventral posteromedial nucleus of the thalamus (VPM) and the periaqueductal gray (PAG) were retrospectively reviewed. Pain was assessed preoperatively and at multiple postoperative time points using the visual analog scale (VAS), the Short-Form McGill Pain Questionnaire-2 (SF-MPQ-2), and the Pain Disability Index (PDI). RESULTS VAS scores significantly decreased from a mean ± SD of 9.0 ± 1.3 preoperatively to 2.6 ± 1.5 at 1 year postoperatively (p = 0.001). PDI scores decreased from a mean total of 48.5 to 28.5 (p = 0.01). SF-MPQ-2 scores decreased from a mean of 4.6 to 2.4 (p = 0.03). Notably, several patients did not experience maximum improvement until 6-9 months postoperatively, correlating with repeated programming adjustments. CONCLUSIONS DBS of the VPM and PAG is a potential therapeutic option for patients suffering from severe, intractable facial pain refractory to other interventions. Improved efficacy may be observed over time with close follow-up and active DBS programming adjustments.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".