P.054 Electroconvulsive therapy and epilepsy: a case report
Bibliographic record
Abstract
Background: Electroconvulsive therapy (ECT) involves the induction of a generalized seizure with an electrical current and has been used worldwide when treating medically refractory psychiatric illness. Here we describe a patient with no prior history or risk factors for epilepsy who developed temporal lobe epilepsy after chronic treatment of ECT. Methods: A 16-year-old right-handed boy with severe refractory depression received ECT treatment every 10 days for 8 months. Six months into his ECT treatment, the patient developed seizures and was admitted to a pediatric epilepsy monitoring unit. Results: Initial clinical events included lightheadedness, diaphoresis, and nausea with associated kaleidoscopic vision changes. Seizures progressed to confusion, fear and paranoia by the time the patient was admitted for monitoring. Long-term video EEG captured many focal seizures with impaired awareness, all originating from both temporal lobes. MRI was normal. ECT was terminated and the patient started on carbamazepine. He has been seizure free for the past 2 years on medication Conclusions: While rare, we present a case of a patient with no prior risk factors for epilepsy who developed temporal lobe epilepsy after chronic ECT treatment. Although ECT is an indispensable treatment for many medically refractory psychiatric illnesses, we suggest caution in young patient undergoing ECT.
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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) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".