The long-term impact of treatment with electroconvulsive therapy on discrete memory systems in patients with bipolar disorder
Bibliographic record
Abstract
OBJECTIVE: Electroconvulsive therapy (ECT) has been controversially associated with long-lasting memory problems. Verbal learning and memory deficits are commonly reported in studies of people with bipolar disorder (BD). Whether memory deficits can be exacerbated in patients with BD who receive ECT has, to our knowledge, not been systematically examined. We aimed to examine whether long-term effects of ECT on discrete memory systems could be detected in patients with BD. METHODS: We studied several domains of memory in 3 groups of subjects who were matched for age and sex: a group of healthy comparison subjects, a group of people with BD who had received ECT at least 6 months before memory assessment and another group with BD that had an equal past illness burden but had never received ECT. Memory was assessed with the California Verbal Learning Test, the Continuous Visual Memory Test and a computerized process dissociation task that examines recollection and habit memory in a single paradigm. RESULTS: Compared with healthy subjects, patients had verbal learning and memory deficits. Subjects who had received remote ECT had further impairment on a variety of learning and memory tests when compared with patients with no past ECT. This degree of impairment could not be accounted for by illness state at the time of assessment or by differential past illness burden between patient groups. CONCLUSIONS: From a clinical perspective, it is unlikely that such findings, even if confirmed, would significantly change the risk-benefit ratio of this notably effective treatment. Nonetheless, they may highlight the importance of attending to cognitive factors in patients with BD who are about to receive ECT; further, they raise the question of whether certain strategies that minimize cognitive dysfunction with ECT should be routinely employed in this patient group.
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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 | 0.000 | 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) | 0.001 | 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".