An Electronic Assessment System of Inflammatory Demyelinating Diseases Based on Digital Drawing Test
Bibliographic record
Abstract
This study developed an electronic cognitive assessment system for patients with Inflammatory Demyelinating Diseases (IDD), aiming to explore the application of digital drawing in assisting cognitive assessment. The system includes the Montreal Cognitive Assessment (MoCA), Symbol Digit Modalities Test (SDMT), and a series of neuropsychological assessment scales. Additionally, it incorporates digital drawing tasks such as Sentence Writing, Pentagon Copying, Cube Copying, Trail Making Test, and Clock Drawing Test. Dynamic drawing data of digital drawing were collected using a digitizing tablet, and features were extracted. A cognitive state classification model was constructed using machine learning methods. Support vector machines (SVM) showed superior classification performance across multiple task datasets, with the best performance observed in the classification of cube drawing tasks. Significance analysis revealed that features such as total time in the Cube Copying, air stroke length in the Cube Copying, and mean of stroke length in the Trail Making Test effectively distinguished the cognitive impairment group (CI) from the cognitive preservation group (CP) and showed significant correlations with neuropsychological assessment scores. This study provides a novel automated assessment tool for detecting cognitive impairment in patients with IDD and offers empirical evidence for future research.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".