The gesture imitation test in dementia with Lewy bodies and Alzheimer's disease dementia
Bibliographic record
Abstract
Objectives Dementia with Lewy bodies (DLB) is the second most common type of neurodegenerative dementia following Alzheimer's disease dementia (ADD). This study investigated the diagnostic role of the gesture imitation test in detecting DLB and differentiating DLB from ADD. Methods A total of 63 patients with DLB, 93 patients with ADD, and 88 healthy controls were included in this study. All participants were administered the gesture imitation test, the Mini-Mental State Examination (MMSE), the Montreal Cognitive Assessment (MoCA), the clock drawing test (CDT), and other neuropsychological tests. Results The patients with DLB performed worse than the healthy controls in the global scores and on every item of the gesture imitation test (p < 0.001). The area under the curve (AUC) for the global scores was 0.889 (p < 0.001) in differentiating the DLB and control groups. Item 4 was a better discriminator, with a sensitivity of 79.37% and a specificity of 79.55%. The AUC for the global scores decreased to 0.593 and the difference was marginal (p = 0.079) in differentiating the DLB and ADD groups. The patients with DLB performed worse on Items 1 and 4 compared with the patients with ADD (p = 0.040, 0.004). The gesture imitation test was positively correlated with the scores of the MMSE (r = 0.355, p = 0.017), the MoCA (r = 0.382, p = 0.010), and the CDT (r = 0.407, p = 0.005) in patients with DLB. Conclusion The gesture imitation test is an easy, rapid tool for detecting DLB and has a role in differentiating DLB from ADD, especially in Items 1 and 4.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".