ECT in bipolar and unipolar depression: differences in speed of response
Bibliographic record
Abstract
OBJECTIVES: There is sparse evidence for differences in response to electroconvulsive therapy (ECT) between patients with bipolar or unipolar major depression, with virtually no information on speed of response. We contrasted a large sample of bipolar (BP) and unipolar (UP) depressed patients in likelihood and rapidity of clinical improvement with ECT. METHODS: Over three double-blind treatment protocols, 228 patients met Research Diagnostic Criteria for UP (n = 162) or BP depression (n = 66). Other than lorazepam PRN (3 mg/day), patients were withdrawn from psychotropics prior to the ECT course and until after post-ECT assessments. Patients were randomized to ECT conditions that differed in electrode placement and stimulus intensity. Symptomatic change was evaluated at least twice weekly by a blinded evaluation team, which also determined treatment length. RESULTS: Patients with BP and UP depression did not differ in rates of response or remission following the ECT course, or in response to unilateral or bilateral ECT. Degree of improvement in Hamilton Rating Scale for Depression scores following completion of ECT was also comparable. However, BP patients received significantly fewer ECT treatments than UP patients, and this effect was especially marked among bipolar ECT responders. Both BP I and BP II patients showed especially rapid response to ECT. CONCLUSIONS: The BP/UP distinction had no predictive value in determining ECT outcome. In contrast, there was a large effect for BP patients to show more rapid clinical improvement and require fewer treatments than unipolar patients. The reasons for this difference are unknown, but could reflect a more rapid build up of anticonvulsant effects in BP patients.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".