Comparison of Cognitive Screening Tools Used to Assess Neurocognitive Side Effects of Electroconvulsive Therapy
Bibliographic record
Abstract
This systematic review identified and critically appraised existing research comparing cognitive screening tools used to assess neurocognitive side effects of electroconvulsive therapy (ECT). A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews (PRISMA) guidelines and the Synthesis Without Meta-analysis (SWiM) reporting guideline. A protocol was registered with the Open Sciences Network (OSF) registry, and critical appraisal was completed using the Joanna Briggs Institute Critical Appraisal tool for Diagnostic Test Accuracy Studies. Pragmatic criteria were applied to assess relative strengths and weaknesses of the identified cognitive screening tools for application to clinical practice. Nine studies met the inclusion criteria; 2 ECT-specific cognitive screening tools, 2 generalised cognitive screening tools, 4 neurocognitive batteries, and 1 brief memory scale. The ECT-specific cognitive screening tools were the Brief ECT Cognitive Screen (BECS) and the ElectroConvulsive Therapy Cognitive Assessment (ECCA). The Montreal Cognitive Assessment (MoCA) was the most common generalised cognitive screening tool used. The BECS and ECCA scored the highest using our specific pragmatic criteria. Key limitations across the included studies were the lack of a suitable gold standard comparator and inadequate blinding of assessors. There is limited evidence to support the superiority of one cognitive tool over another for assessing neurocognitive side effects associated with ECT. The primary limitations across the current literature base are the lack of a gold standard reference tool and heterogeneity of included populations. There is a need for further research to validate the sensitivity and specificity of tools such as the BECS and ECCA.
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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 | 0.024 | 0.097 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.015 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".