Autobiographical Memory Specificity in Major Depression Treated With Electroconvulsive Therapy
Bibliographic record
Abstract
OBJECTIVE: Autobiographical memory in major depression is characterized by reduced specificity, which reflects the tendency to summarize categories of events rather than recall specific instances of events situated in a time and place. This widely studied cognitive marker for depression has not been extensively examined in patients treated with electroconvulsive therapy (ECT). METHODS: We conducted a retrospective chart review of a naturalistic cohort of patients receiving a course of brief-pulse predominantly bitemporal ECT for a major depressive episode. Patients completed the recent life section of the Kopelman Autobiographical Memory Interview (AMI) at pre-ECT baseline, end of treatment course, and 3-month follow-up as part of routine clinical practice. Mood was assessed using the 24-item Hamilton Rating Scale for Depression. RESULTS: We identified 48 patients (mean age, 61.6; female, 62.5%) meeting inclusion criteria. A total of 77.1% of patients responded to the ECT course, 29.7% subsequently relapsed. There were no significant changes over time on either AMI total score or semantic and episodic subscales. However, patients were markedly impaired on episodic autobiographical memory compared with the normative sample at all 3 assessment points, whereas personal semantic memory recall was normal. Specificity of episodic autobiographical memory at baseline did not predict response to ECT or likelihood of relapse. CONCLUSIONS: We found reduced specificity of episodic autobiographical memory in depressed patients before ECT, which persisted at long-term follow-up despite significant improvement in mood. The finding of no detectable retrograde amnesia likely reflects lack of sensitivity of the recent life section of the AMI to detect ECT-induced changes.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".