Directional Deep Brain Stimulation Can Target the Thalamic “Sweet Spot” for Improving Neuropathic Dental Pain
Bibliographic record
Abstract
BACKGROUND: Neuropathic dental pain (NDP) is a chronic pain condition that is notoriously difficult to treat. To date, there are no deep brain stimulation (DBS) studies on this specific pain condition and no optimal target or "sweet spot" has ever been defined. OBJECTIVE: To determine the optimal thalamic target for improving this condition by utilizing the steering abilities of a directional DBS electrode (Vercise CartesiaTM Model DB-2202-45, Boston Scientific). METHODS: A literature search and review of our database identified 3 potential thalamic targets. A directional lead was implanted in a patient with NDP and its current steering used to test the effects in each nucleus. The patient reported her pain after 2 wk of stimulation in a prospective randomized blinded trial of one. Quality of life measurements were performed before and after 3 mo on their best setting. RESULTS: We identified 3 potential nuclei: the centromedian (CM), ventral posterior medial (VPM), and anterior pulvinar. The best results were during VPM stimulation (>90% reduction in pain) and CM stimulation (50% reduction). Following 3 mo of VPM-DBS in combination of lateral CM stimulation, their pain disability index dropped (from 25 to 0) and short form 36 improved (from 67.5 to 90). CONCLUSION: VPM stimulation in combination with CM stimulation is a promising target for NDP. DBS electrode directionality can be used to test multiple targets and select a patient specific "sweet spot" for NDP treatment.
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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) | 0.002 | 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".