Encephalitis and Catatonia Treated With ECT
Bibliographic record
Abstract
OBJECTIVE: To describe 2 cases of encephalitis with neuropsychiatric symptoms including catatonia, compounded by neuroleptic use for delirious agitation culminating in malignant catatonia responsive to electroconvulsive therapy (ECT). BACKGROUND: Neuropsychiatric symptoms including catatonia can be manifestations of limbic encephalitis and encephalitides of unidentified etiology, including encephalitis lethargica. Catatonic features are often difficult to appraise in this context. This can easily lead to the use of neuroleptics, which may precipitate worsening of catatonia. METHOD: Medical, neurologic, and psychiatric histories, physical examination findings, results of laboratory, imaging and neurophysiologic investigations, and treatment response with medications and ECT were recorded. RESULTS: Both patients showed significant improvement with ECT. CONCLUSIONS: Malignant catatonia can complicate encephalitis lethargica and idiopathic limbic encephalitis, which already carry high mortality rates. When neuroleptics are used for agitation in cases of encephalitis, physicians must be wary of precipitating malignant catatonia and neuroleptics should be discontinued when such a danger emerges. Although lorazepam is helpful in treating catatonia, it may not suffice, as in the cases presented. ECT deserves serious consideration early in the course of malignant catatonia and for catatonia nested in encephalopathy secondary to encephalitis, unresolved with lorazepam.
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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 | 0.003 |
| 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 | 0.004 | 0.002 |
| 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".