Worsening of Myasthenia Gravis following the Addition of Gabapentin for Essential Tremor
Bibliographic record
Abstract
Objective: To report a case of worsening myasthenia gravis after the initiation of gabapentin for treatment of essential tremor. Case Summary: A 69-year-old man experienced minor ptosis of the left eyelid, slight neck weakness, and transient difficulties in chewing food on day 6 of a slow gabapentin dosage titration. As the titration continued, the myasthenia symptoms progressed to bilateral ptosis, and there was dramatic worsening of weakness by day 23. He was taking gabapentin 1500 mg daily at the time; the drug was then tapered to discontinuation. All myasthenia gravis symptoms were reported as resolved 3 days after discontinuation of gabapentin. The initiation and discontinuation of gabapentin also coincided with improvement, then worsening of the patient's tremor. Discussion: Three published case reports describe worsening of myasthenia gravis during use of gabapentin for the treatment of various neuropathies. To the best of our knowledge, this is the first case report of a patient experiencing worsening of myasthenia gravis upon initiation of gabapentin for essential tremor. The Naranjo probability scale suggests a probable association between gabapentin and worsening of myasthenia gravis symptoms in our patient. Several myasthenia-like symptoms are listed in the gabapentin mongraph as low-frequency adverse events, increasing the possibility that gabapentin may have been unmasking or worsening myasthenia gravis in clinical trials. However, myasthenia gravis is not identified as a contraindication or caution for gabapentin use. Conclusions: Gabapentin should be used with caution in patients with myasthenia gravis, and clinicians must be aware of the potential risks of this therapy.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".