Reliable Change Indices and Minimum Detectable Change for the Montreal Cognitive Assessment in Electroconvulsive Therapy for Depression
Bibliographic record
Abstract
OBJECTIVE: The Montreal Cognitive Assessment (MoCA) is a commonly used brief cognitive screening tool for monitoring adverse cognitive effects of electroconvulsive therapy (ECT). The aim of this study was to examine three statistical methods for detecting reliable change in the MoCA following ECT. METHODS: In a prospective cohort study, 47 patients (mean age 55.2 [SD = 12.8], 59.6% female) with unipolar or bipolar depression treated with an acute course of brief-pulse ECT (72.3% right unilateral) and 47 depressed controls without ECT exposure were tested on the MoCA at baseline and retested at comparable time intervals. ECT patients' performance was also compared to published normative data from a community-based sample of older adults. We calculated proportions of ECT patients remaining stable, declining, and improving following ECT using practice-corrected reliable change index, standardized regression-based formulas, and minimum detectable change cutoff of ±4 MoCA points. RESULTS: Using the three methods, 72.3%-78.7% of ECT patients remained stable, 17.0%-23.4% declined, and 4.3% improved in MoCA performance following ECT compared to the two control groups. CONCLUSIONS: All three methods yield consistent estimates of reliable change in MoCA scores from pre- to post-brief-pulse ECT. The minimum detectable change approach may be the most efficient and accessible method of detecting change due to simplicity of calculation.
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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 | 0.018 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".