Resolution of ictal bradycardia and asystole following temporal lobectomy: A case report, and review of available cases using pacemakers
Bibliographic record
Abstract
Ictal bradycardia (IB) and ictal asystole (IA) are uncommonly recognized phenomena that increase morbidity in patients with epilepsy by causing syncope and seizure-related falls. These arrhythmias are also suspected to be involved in the pathophysiology of sudden unexpected death in epilepsy (SUDEP). We report a case of a 57-year-old male with left temporal lobe epilepsy who experienced both IB and IA. This patient was initially managed with pacemaker implantation, prior to undergoing left temporal lobectomy. Following surgery, the patient had no ongoing IB or IA on his pacemaker recordings, and his seizure control was greatly improved. His pacemaker was removed approximately one year post-operatively and he continued treatment with anti-seizure drugs (ASDs). A literature review of cases of IB and IA that were managed with pacemakers was performed. Pacemaker implantation appears to be quite effective for reducing seizure-related syncope and falls in the setting of IB/IA. Epilepsy surgery also seems to be an effective treatment option for IB/IA, as many patients are able to have their pacemakers removed post-operatively. Further investigations into the pathophysiology of IB and IA and long-term outcomes using different treatment modalities are clearly needed to help formulate treatment guidelines and, potentially, to reduce the occurrence of SUDEP in these patients.
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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 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".