Clock drawing and neuroanatomical correlates: A systematic review
Bibliographic record
Abstract
OBJECTIVE: The popular clock drawing test (CDT) is easy to administer, acceptable to patients, and has excellent psychometric properties. Although it has been used primarily as a cognitive screening test, many studies have attempted to establish the CDT's ability to localize specific brain lesions or pathology. This systematic review aimed to summarize the evidence on the neuroanatomical correlates of the CDT. METHODS: Using PRISMA guidelines, the authors systematically reviewed the evidence on neuroanatomical correlates of clock drawing by a systematic search in six databases (Pubmed, CINHL, PsychINFO, HealthStar, Embase, and Web of Science) until January 2018. Studies were included if they reported CDT correlations with anatomical brain lesions documented by neuroimaging. RESULTS: Forty-five papers met inclusion criteria. Thirty-one studies identified distinct areas of neuroanatomical correlates of CDT utilizing different scoring methods and imaging techniques. Nine articles reported on the degree of white matter hyperintensities that correlated with lower scores on CDT and the severity of cognitive deficits. Five articles focused on postacute cerebrovascular accidents correlated with CDT performance. A variety of different anatomical lesions, located in all areas of the brain, were associated with abnormalities on the CDT. CONCLUSIONS: The CDT, regardless of scoring method and population studied, was not associated with any consistent, specific brain localization. This systematic review supports the use of the CDT as a cognitive screening test rather than a method of localizing brain lesions.
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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".