Cognitive change after electroconvulsive therapy in mood disorders measured with the Montreal Cognitive Assessment
Bibliographic record
Abstract
OBJECTIVE: The Montreal Cognitive Assessment (MoCA) is a sensitive and clinically practical test but its usefulness in measuring long-term cognitive effects of ECT is unclear. Using the MoCA, we investigated short- and long-term global cognitive change in ECT-treated patients with a Major Depressive Episode (MDE). METHOD: We included 65 consecutive ECT-treated patients with MDE, in whom global cognitive functioning was assessed at baseline (T0); during ECT (before the third session; T1); and 1 week (T2), 3 months (T3), and 6 months (T4) after completion of the index course. Changes in MoCA (sub)scores were analyzed using linear mixed models and reliable change indices were computed to investigate individual changes in MoCA total scores. RESULTS: There was a significant effect of time on MoCA scores (F(4, 230.5) = 4.14, P = 0.003), with an improvement in global cognitive functioning from T3 compared to T1 and T2. At the individual level, 26% (n = 17) of patients showed a significantly worse cognitive functioning at T2 and 12% (n = 8) an improved cognitive functioning compared to T0. For T4, these percentages ameliorated to 8% and 18% respectively. CONCLUSION: No persistent global cognitive impairment induced by ECT was found at the group level using the MoCA. At the individual level, however, there was clear heterogeneity in the effects of ECT on cognitive functioning. The MoCA is a suitable tool to monitor short- and long-term global cognitive functioning in ECT-treated patients with MDE but in younger patients, potential ceiling effects must be taken into account.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".