Neuropsychological test performance in mild cognitive impairment with Lewy bodies: A systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: We sought to characterize the cognitive profile among individuals with mild cognitive impairment with Lewy bodies (MCI-LB) to help guide future clinical criteria. METHODS: Systematic review and meta-analysis included MCI-LB studies with cognitive data from PubMed, Embase, Web of Science, and PsycINFO (January 1990 to March 2023). MCI-LB scores were compared to controls, MCI due to Alzheimer's disease (MCI-AD), and dementia with Lewy bodies (DLB) groups with random-effects models. RESULTS: We included 26 studies and 2823 participants. Across all domains, the MCI-LB group performed worse than controls and better than DLB. Compared to MCI-AD, the MCI-LB group performed worse in attention/processing speed (g = -0.24, 95% confidence interval [CI]: -0.35, -0.12), attention/executive (g = -0.42, 95% CI: -0.56, -0.28); better in verbal immediate recall (g = 0.37; 95% CI: 0.15, 0.59) and delayed memory (g = 0.40; 95% CI: 0.22, 0.58). DISCUSSION: The cognitive profiles in MCI-LB and MCI-AD are consistent with established profiles in DLB and AD. Neuropsychological assessment may be helpful in differential diagnosis, even in early disease states. HIGHLIGHTS: We performed a systematic review and meta-analysis for cognition in mild cognitive impairment with Lewy bodies (MCI-LB). Compared to MCI due to Alzheimer's disease (MCI-AD), MCI-LB had worse attention, executive function, and processing speed. Compared to MCI-AD, MCI-LB had better verbal immediate and delayed recall. The MCI-LB group was worse on all cognitive domains than controls, and better than dementia with Lewy bodies. Studies used different tests and there is a need for global efforts for harmonization.
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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.024 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".