Creutzfeldt–Jakob disease-like syndrome induced by gabapentin toxicity
Bibliographic record
Abstract
Patients with Creutzfeldt–Jakob disease (CJD) may exhibit characteristic abnormalities on the electroencephalogram (EEG). However, these abnormalities have been associated with a number of cases of drug toxicity. We report a case of CJD-like syndrome associated with gabapentin. A 78-year-old man was hospitalized for recurrent falls. Three months prior to admission, gabapentin was prescribed to treat symptoms of trigeminal neuralgia. The patient subsequently presented with a two-month history of worsening gait abnormalities, negative myoclonus, and cognitive impairment. The EEG showed diffuse background slowing with larger amplitude delta discharges, which at times appeared triphasic, raising the possibility of CJD. The gait abnormalities and myoclonus resolved and the EEG normalized after the gabapentin was discontinued. Several cases of drug-induced CJD-like syndrome have been reported, mainly presenting with cognitive impairment, myoclonus, Parkinsonism, and EEG abnormalities. This patient may have been predisposed to adverse neurological effects from gabapentin owing to age, concurrent renal insufficiency, and cardiac disease. We concluded that it is imperative to include drug toxicity in the differential diagnosis of patients presenting with clinical manifestations and EEG findings suggestive of CJD, particularly in the setting of advanced age and comorbidities.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".