Clock‐drawing test: Normative data of three quantitative scoring methods for Chinese‐speaking adults in Shijiazhuang City and clinical utility in patients with acute ischemic stroke
Bibliographic record
Abstract
OBJECTIVE: The clock-drawing test (CDT) is a widely used screening tool for detecting cognitive decline. However, normative data for Chinese individuals are scarce. Our study aimed to provide standardized values for the three quantitative CDT scoring methods that were tailored for Chinese-speaking adults in Shijiazhuang City and explore the discriminant validity of the CDT scores in patients with acute ischemic stroke. METHODS: We conducted the CDT among 418 healthy individuals aged between 35 and 84 years. The CDT was administered and scored by five raters using the method derived from the Montreal Cognitive Assessment (MoCA), Rouleau's, and Babins' scoring systems. The influence of age, education, and sex on the performance in the CDT was analyzed. Furthermore, 336 patients with acute ischemic stroke were enrolled to explore the discriminant validity of CDT scores. RESULTS: In all three scoring systems, CDT scores were significantly correlated with age and years of education but not with sex. Normative data stratified for age and years of education were established. Interrater and intersystem reliability were high in our study. CDT total scores and subscores showed significant differences between stroke patients and healthy individuals. CONCLUSIONS: Our study provides CDT normative data using three quantitative scoring methods for Chinese-speaking adults in Shijiazhuang City. Age and education level were the key factors that affected the CDT scores. CDT total scores and subscores provided good discriminant validity for patients with acute ischemic stroke.
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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) | 0.000 | 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".