Psychometric Properties of the Chinese Version of the ElectroConvulsive Therapy Cognitive Assessment
Bibliographic record
Abstract
OBJECTIVES: Electroconvulsive therapy (ECT) is an effective somatic treatment, but it may be limited by cognitive adverse effects. The existing cognitive screening instruments often lack specificity to ECT-associated cognitive deficits. The ElectroConvulsive Therapy Cognitive Assessment was developed and validated in a clinical setting, but the reliability and validity of the Chinese version of ElectroConvulsive Therapy Cognitive Assessment (ECCA-C) have not been studied in a large clinical sample. METHODS: The ECCA-C and the Montreal Cognitive Assessment (MoCA) were administered to patients with major depressive disorder (MDD) undergoing ECT at 3 time points: pretreatment (baseline), before the fifth treatment, and 1 week posttreatment. The instruments were also administered to a sample of healthy subjects. RESULTS: Sixty-five patients with MDD and 50 age- and sex-matched healthy controls were recruited in this study. Overall, the patient group had statistically significantly lower MoCA and ECCA-C scores than the control group (both P values <0.001). The Cronbach α of the ECCA-C was 0.88 at baseline. Statistically significant decreases over time were observed in ECCA-C: pre-ECT (23.9 ± 4.0) > mid-ECT (21.3 ± 3.4) > post-ECT (18.7 ± 4.8) (all P values <0.001), whereas no statistically significant changes in MoCA scores were found at these 3 time points ( F = 1.86, P = 0.165). A cutoff score of 26.5 on the ECCA-C was found to best differentiate between MDD patients and healthy controls. CONCLUSIONS: The ECCA-C showed satisfactory psychometric properties and may be a more sensitive instrument than the MoCA to assess cognitive impairment associated with ECT.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".