Electroconvulsive Therapy-Responsive Catatonia in a Medically Complicated Patient
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it