Clock Drawing as a Tool to Reduce Cognitive Assessment Time
Bibliographic record
Abstract
BACKGROUND: The Clock Drawing Test (CDT) is a widely used neuropsychological tool and a component of the Toronto Cognitive Assessment (TorCA). We aimed to determine whether specific CDT sub-scores predict performance on other TorCA sub-tests. Identifying such relationships may support removal of measures, thereby reducing overall TorCA administration time. METHOD: Data were obtained from the Toronto Dementia Research Alliance database which includes patient demographic and clinical information assessed at four Toronto area memory clinics. Performance on the CDT is based upon 4 sub-scores: contour, numbers, hands, and centre. The TorCA contains 24 sub-tests in addition to the CDT that evaluate cognitive domains including memory, visuospatial, working memory/attention/executive control, and language. To identify a linear combination of CDT sub-scores that is maximally associated with a linear combination of the other TorCA sub-tests, we used singular value decomposition of their cross-block correlation matrix as applied by Partial Least Squares. Reported saliences describe relative contributions of each variable to the linear combinations. Saliences greater than 2 standard errors (se) are considered significant. RESULT: CDT and TorCA sub-test saliences using data from 1,872 participants are presented in Figure 1. The "hand" sub-score returned relatively large salience (0.71, se = 0.31). The "numbers" sub-score had slightly lower salience (0.50, se = 0.22); the "centre" sub-score returned a similar salience (0.43, se = 0.19). TorCA sub-tests of working memory/attention/executive control, semantic knowledge, and visuospatial function returned larger saliences (Figure 1). Specifically, the strongest association was with Trails B (0.33, se = 0.15), followed by Trails A (0.27, se = 0.13), semantic fluency (0.25, se = 0.11), Benson Figure copy (0.25, se = 0.12) and recall (0.25, se = 0.11). CONCLUSION: Within the CDT scoring system, clock hands and numbers were the strongest predictors of performance on the TorCA sub-tests. The most robust associations were with domains of working memory/attention/executive function, semantic knowledge, and visuospatial function. We plan to apply artificial intelligence to classify clocks based on these cognitive functions. We will then examine how these classified clocks relate to TorCA sub-tests to determine whether redundant tests could be removed, thereby shortening administration time.
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 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 | 0.012 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".