<scp>ECT</scp> beyond unipolar major depression: systematic review and meta‐analysis of electroconvulsive therapy in bipolar depression
Bibliographic record
Abstract
OBJECTIVE: In this systematic review and meta-analysis, the response, remission, and speed of response in adults with major depressive disorder (MDD) and bipolar disorder in depressive episode (BDD) receiving an acute course of electroconvulsive therapy (ECT) were quantitatively analyzed. METHODS: Using the Preferred Reporting Items for Systematic Reviews and Meta-analysis guidelines, 1660 citations were identified through five electronic databases. Nineteen articles met final inclusion criteria for meta-analysis. RESULTS: The pooled response and remission rates with ECT in MDD were 74.2% (n = 1246/1680) and 52.3% (n = 850/1626), respectively. In BDD, they were 77.1% (n = 437/567) and 52.3% (n = 275/377), respectively. Although response rates to ECT were statistically higher in BDD (OR = 0.73, 95% CI: 0.56-0.95, P = 0.02), remission rates were similar (OR = 0.91, 95% CI: 0.65-1.26, P = 0.56). Individuals with BDD vs. MDD required fewer number of ECT sessions to achieve response (SMD = -0.23, 95% CI: -0.44 to -0.023, P = 0.03). There were no significant moderator effects identified. CONCLUSION: Response rates and speed of response are higher in individuals with BDD; however, remission rates are equivalent. These findings support increased utilization of ECT in individuals with treatment-refractory BDD.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.027 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".