In-Hospital Management of Acute Trigeminal Neuralgia Pain Crises
Bibliographic record
Abstract
Trigeminal neuralgia is the most common form of craniofacial neuropathic pain with an incidence of 4 to 29 per 100,000 people per year. Acute trigeminal neuralgia pain crises are characterized by increased pain frequency and severity and can impact oral intake and sleep, as well as mood. The diagnosis of acute trigeminal neuralgia is clinical and supported by magnetic resonance imaging demonstrating morphological changes in the trigeminal neurovascular bundle on the ipsilateral side of the pain. Patients often present to the hospital seeking relief from acute exacerbations, making it essential for physicians to understand the management of an acute pain crisis, which differs from the chronic management, especially as there may be limited neurology or pain specialist support after hours. The need for improved knowledge of the treatment of acute trigeminal neuralgia is evidenced by opioids being the most prescribed analgesia despite little efficacy in treating it, a lack of evidence supporting their use and concerning side-effects. This article summarizes the evidence behind pharmacological therapy with fosphenytoin, phenytoin, and lidocaine as rescue medications in acute trigeminal neuralgia through the case of a male patient, age 58 years, who experienced complete resolution of pain following administration of phenytoin.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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".