Electroconvulsive Therapy-Responsive Catatonia in a Medically Complicated Patient
Bibliographic record
Abstract
BACKGROUND: Profoundly depressed states of awareness classified as either catatonia or akinetic mutism have been reported in patients with various general medical conditions including encephalitis, frontal lobe tumors, or paraneoplastic limbic encephalitis. Catatonic features are often difficult to apprise in this context. This can result in electroconvulsive therapy (ECT) discontinuation, although it remains the most effective treatment of catatonia. CASE REPORT: We describe the case of a patient with a history of unresectable right retroorbital squamous cell carcinoma, status poststereotactic radiation and cisplatin, and subsequent pneumococcal meningitis of the temporal lobe with abscess formation who became catatonic after receiving 3 bitemporal treatments with ECT for severe depression and whose catatonia improved with continued ECT. Furthermore, she demonstrated progressive improvement in mood, interactivity, and overall neurologic function after ECT treatment was completed. CONCLUSIONS: The search for an etiology of a profound catatonic state should include the probability of underlying medical disorder. Although lorazepam may be helpful in some cases, ECT deserves early consideration in catatonia, especially in cases where the underlying cause seems to be uncertain, even if the catatonia begins in the midst of treatment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".