Short-lasting unilateral neuralgiform headache attacks: case reports and review
Bibliographic record
Abstract
BackgroundShort-lasting unilateral neuralgiform headache attacks are the rarest type of trigeminal-autonomic cephalalgias (TACs), typically classified into two subtypes: SUNCT (short-lasting unilateral neuralgiform headache attacks with conjunctival injection and tearing) and SUNA (with cranial autonomic symptoms). These disorders are characterized by brief, unilateral headache attacks accompanied by cranial parasympathetic symptoms and can be difficult to diagnose and manage in clinical practice.ObjectiveTo present two clinical cases of SUNCT and provide a comprehensive review of the current diagnostic criteria, pathophysiology, and treatment options, while distinguishing SUNCT/SUNA from other headache syndromes.MethodsWe describe two patients diagnosed with SUNCT at our outpatient headache clinic. A literature review was conducted focusing on clinical features, diagnostic criteria from the International Classification of Headache Disorders (ICHD-3), proposed pathophysiological mechanisms, and available treatment strategies.ResultsBoth patients presented with multiple daily, short-lasting, unilateral headache attacks accompanied by cranial autonomic symptoms. Diagnostic workup ruled out secondary causes. The clinical presentation aligned with SUNCT criteria as defined in ICHD-3. Treatment approaches varied and included pharmacological interventions with partial symptom relief. The review highlights the challenges in differentiating SUNCT/SUNA from other headache disorders such as trigeminal neuralgia and cluster headache.ConclusionAccurate recognition of SUNCT/SUNA is essential for appropriate management, as misdiagnosis is common due to symptom overlap with other headache syndromes. Improved awareness and understanding of these rare conditions can lead to better diagnosis, targeted treatment, and 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".