Bibliographic record
Abstract
Relapse of severe depression after successful treatment with electroconvulsive therapy (ECT) continues to be a major problem. We review the literature on relapse after ECT and factors that predict relapse. Early studies showed that the relapse rate was approximately 50% without follow-up treatment and that the majority of these relapses occurred in the first 6 months. More recent studies have found even higher rates in delusional depression and possibly in "double depression." Studies of biological markers as predictors of relapse were examined. Six of nine studies of the dexamethasone suppression test and one study of cortisol hypersecretion show that post-ECT nonsuppressors are at higher risk; although insensitive for diagnostic purposes, this test may be useful, when persistently abnormal, as a predictor of relapse. Studies of the thyrotropin-releasing hormone stimulation test and shortened rapid eye movement sleep latency are inconclusive. Medication resistance pre-ECT has been shown to predict relapse in two studies and highlights the need for more aggressive and effective treatment in this group. Further research into the prediction and prevention of depressive relapse after ECT is needed, and the field anxiously awaits current trials comparing ECT with combination lithium and nortriptyline.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".