A reduced scoring system for the Clock Drawing Test using a population-based sample
Bibliographic record
Abstract
BACKGROUND: Many scoring systems exist for clock drawing task variants, which are common dementia screening measures, but all have been derived from clinical samples. This study evaluates and combines errors from two published scoring systems for the Clock Drawing Test (CDT), the Lessig and Tuokko methods, in order to create a simple yet optimal scoring procedure to screen for dementia using a Canadian population-based sample. METHODS: Clock-drawings from 356 participants (80 with dementia, 276 healthy controls) from the Canadian Study on Health and Aging were analyzed using logistic regression and Receiver Operating Characteristic curves to determine a new, simplified, population-based CDT scoring system. The new Jouk scoring method was then compared to other commonly used systems (e.g. Shulman, Tuokko, Watson, Wolf-Klein). RESULTS: The Jouk scoring system reduced the Lessig system even further to include five critical errors: missing numbers, repeated numbers, number orientation, extra marks, and number distance, and produced a sensitivity of 81% and a specificity of 68% with a cut-off score of one error. With regard to other traditionally used scoring methods, the Jouk procedure had one of the most balanced sensitivities/specificities when using a population-based sample. CONCLUSIONS: The results from this study improve our current state of knowledge concerning the CDT by validating the simplified scoring system proposed by Lessig and her colleagues in a more representative sample to mimic conditions a general clinician or researcher will encounter when working among a wide-ranging population and not a dementia/memory clinic. The Jouk CDT scoring system provides further evidence in support of a simple and reliable dementia-screening tool that can be used by clinicians and researchers alike.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".