Treatment Outcomes and MRI Features of SUNA: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Abstract Background Trigeminal autonomic cephalalgias (TACs), including short-lasting unilateral neuralgiform headache attacks with cranial autonomic symptoms (SUNA), are rare but debilitating headaches. The pathophysiology and optimal treatment of SUNA remain poorly defined, compounded by disparities in healthcare access. Objectives To systematically review and analyze the effectiveness of treatment options, clinical outcomes, and brain MRI findings for SUNA, and to identify gaps in the current evidence base. Methodology Following PRISMA guidelines, a systematic search was performed across multiple databases. Data from 20 studies were analyzed, focusing on treatment efficacy, patient demographics, and MRI findings. Meta-analyses were conducted on treatment effectiveness, and bias was assessed using the Newcastle-Ottawa Scale. Results Among 267 patients, the most commonly used treatments were lamotrigine (37.07%) and greater occipital nerve (GON) block (16.85%), showing effectiveness in over 50% of cases. Heterogeneity in lamotrigine effectiveness was high (Cochran’s Q = 63.10, p-value < 0.0001, α = 0.05). Lidocaine was effective for acute attacks (> 80%). Brain MRIs were mostly unremarkable, with some evidence suggesting neurovascular involvement. Conclusion Lamotrigine and GON block are effective for SUNA, though treatment responses vary widely. MRI findings often lack abnormalities, suggesting a need for further research into the pathophysiology of SUNA. Larger, high-quality studies are needed to establish standardized treatment protocols and improve patient outcomes.
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.034 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".