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Record W7116929601 · doi:10.1002/alz70861_108482

Clock Drawing as a Tool to Reduce Cognitive Assessment Time

2025· article· en· W7116929601 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueAlzheimer s & Dementia · 2025
Typearticle
Languageen
FieldNeuroscience
TopicSpatial Neglect and Hemispheric Dysfunction
Canadian institutionsToronto Western HospitalCentre for Addiction and Mental HealthUniversity Health NetworkUniversity of TorontoToronto Dementia Research AllianceBaycrest HospitalMount Sinai HospitalOccupational Cancer Research CentreSunnybrook HospitalPublic Health Ontario
Fundersnot available
KeywordsCognitionCognitive Assessment SystemNeuropsychological assessmentPlan (archaeology)Task (project management)Test (biology)

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.251
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.023
GPT teacher head0.315
Teacher spread0.292 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it