Normative Scores on the Clock Drawing Test Among Older Adults from a Large Population Survey in Norway: The HUNT Study
Bibliographic record
Abstract
Background: The Clock Drawing Test (CDT) is used to screen for Alzheimer's disease and other dementia disorders. Normative scores on the version from the Montreal Cognitive Assessment (MoCA) do not exist in the Nordic countries. Objective: To examine the normative scores of the CDT among adults aged 70 years and older. Methods: We included 4,023 cognitively healthy persons aged 70-97 years from a population survey in Norway. They were examined with the CDT, which has a total score between zero and three. A multiple multinominal regression model was applied with a CDT score as the dependent categorical variable and estimated the probabilities of scoring a particular score, stratified by age, sex, and education. These probabilities correspond to an expected proportion of the normative population scoring at, or below a given percentile. Results: None scored zero, 2.1% scored one, 14.9% scored two, and 83% scored three. Higher age, female sex and fewer years of schooling were associated with poorer performance. Scores of zero and one deviated from the normative score regardless of age, sex and education. A score of two was within the norm for a female older than 81 and a male older than 85. Conclusions: The majority (83%) of people 70 years and older had a score of three on the CDT. Lower age, male sex, and higher education were associated with a better performance. Scores of zero and one were below the normative score. Except for the very old, a score of two was also well below the normative score.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".