Low medical morbidity and mortality after acute courses of electroconvulsive therapy in a population‐based sample
Bibliographic record
Abstract
BACKGROUND: To determine event rates for specific medical events and mortality among individuals receiving electroconvulsive therapy (ECT). METHOD: Population-based cohort study using health administrative data of acute ECT treatments delivered in Ontario, Canada, from 2003 to 2011. We measured the following medical event rates, per 10 000 ECT treatments, up to 7 and 30 days post-treatment: stroke, seizure, acute myocardial infarction, arrhythmia, pneumonia, pulmonary embolus, deep vein thrombosis, gastrointestinal bleeding, falls, hip fracture, and mortality. RESULTS: A total of 135 831 ECT treatments were delivered to 8810 unique patients. Overall medical event rates were 9.1 and 16.8 per 10 000 ECT treatments respectively. The most common medical events were falls (2.7 and 5.5 per 10 000 ECT treatments) and pneumonia (1.8 and 3.8 per 10 000 ECT treatments). Fewer than six deaths occurred on the day of an ECT treatment. This corresponded to a mortality rate of less than 0.4 per 10 000 treatments. Deaths within 7 and 30 days of an ECT treatment, excluding deaths due to external causes (e.g., accidental and intentional causes of death), were 1.0 and 2.4 per 10 000 ECT treatments respectively. CONCLUSION: Morbidity and mortality events after ECT treatments were relatively low, supporting ECT as a low-risk medical procedure.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".