Factors Associated With Delirium Following Electroconvulsive Therapy
Bibliographic record
Abstract
OBJECTIVES: Delirium following electroconvulsive therapy (ECT) has been a clinical challenge, which, however, has not been investigated through a systematic literature review. The objective of this study was to systematically synthesize available evidence regarding factors associated with post-ECT delirium. METHODS: We conducted a systematic literature search for any type of original investigations that reported risk factors of post-ECT delirium, using PubMed. RESULTS: The literature search identified 43 relevant articles. One study found an association between catatonic feature and increased risk of postictal delirium. Five studies reported that the presence of cerebrovascular disease, Parkinson disease, or dementia was related to higher incidence of post-ECT delirium. Incidence of post-ECT course delirium was increased with bitemporal stimulation (3 studies). One study showed that ultrabrief pulse ECT reduced reorientation time following seizure compared with brief pulse ECT. High stimulus intensity resulted in more prolonged reorientation time after ECT than lower stimulus intensity (2 studies). Longer seizure length was significantly associated with post-ECT delirium in 1 study. Eight studies that examined postictal delirium in association with medications used, including lithium, did not show any consistent finding in their relationships. Four studies showed decreased incidence of postictal delirium in those receiving dexmedetomidine. CONCLUSIONS: Limited evidence suggests that catatonic feature, cerebrovascular disease, Parkinson disease, dementia, bitemporal electrode placement, high stimulus intensity, or longer seizure length are associated with an increased risk of post-ECT delirium. Moreover, dexmedetomidine and ultrabrief pulse ECT seem to have preventive effects of post-ECT delirium.
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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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".