Usefulness of Cube Copying in Evaluating Clinical Profiles of Patients with Parkinson Disease
Bibliographic record
Abstract
OBJECTIVE: To clarify the relationship between the quantitatively assessed cube-copying test (CCT) and clinical profiles of cognitive and motor ability in Chinese patients with Parkinson disease (PD). METHODS: We gave the Montreal Cognitive Assessment (MoCA), which includes the CCT, to evaluate the cognitive function of 102 outpatients with PD. We also gave the Unified Parkinson's Disease Rating Scale (UPDRS) II and III and the Hoehn-Yahr scale to evaluate the patients' motor function and disease severity, respectively. We used Maeshima's method for quantitative assessment of the CCT, and calculated CCT errors by adding incomplete connections and plane-drawing errors. We divided the patients into 2 groups based on normal (no errors) versus abnormal (≥1 errors) CCT scores. RESULTS: We found 34 patients with normal scores and 68 with abnormal scores. The 2 groups had significant differences in age of onset, MoCA score, UPDRS II and III scores, and cognitive deterioration rate. CCT errors correlated inversely with cognitive domains except for orientation. Executive function was most commonly affected in both groups. We found correlations between numbers of CCT errors and left-limb movement, fine hand movement, postural instability and gait disorders, UPDRS II and III scores, and cognitive and motor deterioration rates. CONCLUSIONS: The quantitatively assessed CCT may be useful in estimating cognitive and motor dysfunction in patients with PD, and in monitoring disease progression.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".