The Gesture Imitation in Alzheimer’s Disease Dementia and Amnestic Mild Cognitive Impairment
Bibliographic record
Abstract
BACKGROUND: Alzheimer's disease dementia (ADD) has become an important health problem in the world. Visuospatial deficits are considered to be an early symptom besides memory disorder. OBJECTIVES: The gesture imitation test was devised to detect ADD and amnestic mild cognitive impairment (aMCI). METHODS: A total of 117 patients with ADD, 118 with aMCI, and 95 normal controls were included in this study. All participants were administered our gesture imitation test, the Mini-Mental State Examination (MMSE), the Montreal Cognitive Assessment (MoCA), the Clock Drawing Test (CDT), and the Clinical Dementia Rating Scale (CDR). RESULTS: Patients with ADD performed worse than normal controls on global scores and had a lower success rate on every item (p < 0.001). The area under the curve (AUC) for the global scores when comparing the ADD and control groups was 0.869 (p < 0.001). Item 4 was a better discriminator with a sensitivity of 84.62% and a specificity of 67.37%. The AUC for the global scores decreased to 0.621 when applied to the aMCI and control groups (p = 0.002). After controlling for age and education, the gesture imitation test scores were positively correlated with the MMSE (r = 0.637, p < 0.001), the MoCA (r = 0.572, p < 0.001), and the CDT (r = 0.514, p < 0.001) and were negatively correlated with the CDR scores (r = -0.558, p < 0.001). CONCLUSIONS: The gesture imitation test is an easy, rapid tool for detecting ADD, and is suitable for the patients suspected of mild ADD and aMCI in outpatient clinics.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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".