Using Peripheral Stimulation to Reduce thePain of C2-Mediated Occipital Headaches: APreliminary Report
Bibliographic record
Abstract
BACKGROUND: Peripheral nerve stimulation (PNS) is an accepted treatment for neuropathic pain. Recent studies have focused on its potential for relieving headache pain. OBJECTIVES: To investigate the effectiveness of PNS in reducing occipital headache pain. DESIGN: A prospective, 12-week pilot study involving 11 patients evaluated before and after implantation of PNS systems to treat C2-mediated occipital headaches. METHODS: Prior to and at 4 and 12 weeks after implantation, patients completed the Short-Form McGill Pain Questionnaire (SF-MPQ), Visual Analog Scale (VAS), and Present Pain Index (PPI). Patients also answered questionnaires and kept diaries to record stimulator use, medication consumption, and numbers of headaches. RESULTS: A comparison of pre- and post-implantation evaluations showed statistically significant declines in scores on the SF-MPQ (64%; p = 0.0013), VAS (67%; p < 0.0001), and PPI (68%; p = 0.0009). Most patients (91% and 64% respectively) reported reductions in medication use and numbers of headaches. Patients also reported a reduction in headache symptoms and the impact of headaches on activities. Two adverse events were encountered, one due to a loose connection and, the other caused by lead migration. CONCLUSIONS: PNS reduced headache pain, headache frequency and medication use.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".