Cerebrovascular Steal Phenomenon and Electroconvulsive Therapy
Bibliographic record
Abstract
Electroconvulsive therapy (ECT) is a safe and effective treatment for major depressive disorder, but cerebrovascular and cardiovascular complications, although rare, remain the most concerning. This is particularly notable in those with preexisting cerebrovascular disease, which impacts dynamic cerebral autoregulation. In these patients, the increased blood flow to the seizing portions of the brain induced by ECT potentially can reduce cerebral blood flow to ischemic areas, possibly causing adverse neurological events. The authors describe a patient with chronic cerebral ischemic disease, chronic anemia, and major depressive disorder undergoing ECT to achieve remission. The patient developed recurrent focal neurological deficits after each ECT procedure, with neurological recovery within 48 hours post-ECT. Clinical guidelines may need to be updated for the management of ECT patients with cerebrovascular disease who may be at an increased risk of intraictal and possibly postictal regional ischemia, especially in areas already compromised by a prior stroke and/or by reduced cerebral oxygenation caused by symptomatic anemia at risk of ischemia. Research is needed to assess changes in regional cerebral blood flow during and after ECT in patients with cerebrovascular disease, including small-vessel cerebral ischemia, and to evaluate these changes in relation to the location, intensity, and duration of induced seizure.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".