Efficacy of Occipital Nerve Stimulation in Trigeminal Autonomic Cephalalgias: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Trigeminal autonomic cephalalgias (TACs) are a group of highly disabling primary headache disorders. Although pharmacological treatments exist, they are not always effective or well tolerated. Occipital nerve stimulation (ONS) is a potentially effective surgical treatment. OBJECTIVE: To perform a systematic review of the efficacy of ONS in treating TACs. METHODS: A systematic review was performed using Medline, Embase, and Cochrane databases. Primary outcomes were reduction in headache intensity, duration, and frequency. Secondary outcomes included adverse event rate and reduction in medication use. Because of large differences in outcome measures, data for patients suffering from short-lasting, unilateral, and neuralgiform headache attacks with conjunctival injection and tearing (SUNCT) and cranial autonomic symptoms (SUNA) were reported separately. Risk of bias was assessed using the NIH Quality Assessment Tools. RESULTS: A total of 417 patients from 14 published papers were included in the analysis, of which 15 patients were in the SUNCT/SUNA cohort. The mean reduction in headache intensity and duration was 26.2% and 31.4%, respectively. There was a mean reduction in headache frequency of 50%, as well as a 61.2% reduction in the use of abortive medications and a 31.1% reduction in the use of prophylactic medications. In the SUNCT/SUNA cohort, the mean decrease in headache intensity and duration was 56.8% and 42.8%. The overall responder rate, defined as a >50% reduction in attack frequency, was 60.8% for the non-SUNCT/non-SUNA cohort and 66.7% for the SUNCT/SUNA cohort. Adverse events requiring repeat surgery were reported in 33% of cases. Risk of bias assessment suggests that articles included in this review had reasonable internal validity. CONCLUSION: ONS may be an effective surgical treatment for approximately two thirds of patients with medically refractory TACs.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".