Ictal Asystole and Ictal Syncope
Bibliographic record
Abstract
BACKGROUND: Ictal asystole is a rare, serious, and often treatable cause of syncope. There are currently limited data to guide management. Characterization of ictal syncope predictors may aid in the selection of high-risk patients for treatments such as pacemakers. METHODS AND RESULTS: We searched our epilepsy monitoring unit database from October 2003 to July 2013 for all patients with ictal asystole events. Clinical, electroencephalogram, and ECG data for each of their seizures were examined for their relationships with ictal syncope events. In 10 patients with ictal asystole, we observed 76 clinical seizures with 26 ictal asystole episodes, 15 of which led to syncope. No seizure with asystole duration≤6 s led to syncope, whereas 94% (15/16) of seizures with asystole duration>6 s led to syncope (P=0.02). During ictal asystole events, 4 patients had left temporal seizure onset, 4 patients had right temporal seizure onset, and 2 patients had both. Syncope was more common with left temporal (40%) than with right temporal seizures (10%; P=0.002). Treatment options included antiepileptic drug changes, epilepsy surgery, and pacemaker implantation. Eight patients received pacemakers. During follow-up of 72±95 months, all patients remained syncope free. CONCLUSIONS: Ictal asystole>6 s is strongly associated with ictal syncope. Ictal syncope is more common in left than in right temporal seizures. A permanent pacemaker should be considered in patients with ictal syncope if they are not considered good candidates for epilepsy surgery.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".