The Prevalence of Electroconvulsive Therapy Use Since 1973
Bibliographic record
Abstract
OBJECTIVES: A formal meta-analysis of the use of electroconvulsive therapy (ECT) has never been conducted before in literature reviews or syntheses. Such a study would be hampered by heterogeneity and potential reporting biases. However, it would provide a single comparable measure to allow an analysis of statistical key dimensions such as trends across time and psychiatric resources available. It would also help planners and decision makers to set standards and benchmarks for national and regional guidelines for quality assurance and research in health services. METHODS: We surveyed different databases for relevant studies, limited from 1973 to October 2013. Data were extracted independently by 4 reviewers. The articles retrieved were peerreviewed studies (data-based studies or surveys) presenting ECT population rates (annual patient rates calculated from the general population) or number of patients receiving ECT during or after 1973 and attending a psychiatric establishment (either hospitals or approved ECT delivery centers for inpatients and outpatients in well-defined geographic areas). RESULTS: This meta-analysis includes a total of 18 studies from 12 countries. A composite event rate of 16.9/100,000 inhabitants emerged, characterized by high heterogeneity. Across the countries assessed, the prevalence of ECT was higher in older studies. CONCLUSIONS: By its prevalence, ECT remains rare to exceptional as a specialist treatment for mental disorders. Heterogeneity across regions or countries could best be explained by insufficient standardization of ECT procedures and practices. Linked health databases and audits could help strengthen the effectiveness of ECT in relation to primary outcomes such as suicide and help determine the gap in ECT provision, if any.
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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 | 0.021 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.029 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 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".