ECT on a world map - a narrative review of the use of electroconvulsive therapy and its frequency in the world
Bibliographic record
Abstract
Abstract Introduction: Since implementation, electroconvulsive therapy has remained very effective treatment in psychiatry. The aim of this study is to present the differences in its use in medical practice around the world. The range of diseases in which ECT is most commonly used and the frequency of its use in different countries were compared with special attention to the differences between highly and poorly developed countries. Material and method: Review of literature by searching PubMed and Google Scholar databases using the keywords: indications of ECT, frequency of ECT use for papers published from 1991 to 2021. Results: Among the diseases for which electroconvulsive therapy is used worldwide, major depression dominates, while in Asia and Africa this therapy is used in schizophrenia. In Latin America it is used primarily for schizophrenia and bipolar disorder. In Poland, it is used for depression, bipolar disorder, and fewer for schizophrenia. The highest rate of people treated with therapy per 100,000 population is found in countries such as the USA (51), Canada (23.2-25.6), Australia (37.85), Sweden (41), Finland (23), Slovakia (29.2), Estonia (27.8) and Belgium (47). Conclusions: There is a relationship between the range of diseases most frequently treated with ECT, the frequency of use and the level of country development. In the high developed countries, ECT is used mainly in major depression, in less developed countries more frequent treatment of schizophrenia may be determined by the high cost of medications and limited availability of hospital beds. The highest rates of use of this therapy are found in more developed countries.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".