Limits on using the clock drawing test as a measure to evaluate patients with neurological disorders
Bibliographic record
Abstract
BACKGROUND: The Clock Drawing Test (CDT) is used as a quick-to-conduct test for the diagnosis of dementia and a screening tool for cognitive impairments in neurological disorders. However, the association between the pattern of CDT impairments and the location of brain lesions has been controversial. We examined whether there is an association between the CDT scores and the location of brain lesions using the two available scoring systems. METHOD: One hundred five patients with brain lesions identified by CT scanning were recruited for this study. The Montreal Cognitive Assessment (MoCA) battery including the CDT were administered to all partcipants. To score the CDT, we used a qualitative scoring system devised by Rouleau et al. (1992). For the quantitative scoring system, we adapted the algorithm method used by Mendes-Santos et al. (2015) based on an earlier study by Sunderland et al. (1989). For analyses, a machine learning algorithm was used. RESULTS: Remarkably, 30% of the patients were not detected by the CDT. Quantitative and qualitative errors were categorized into different clusters. The classification algorithm did not differentiate the patients with traumatic brain injury 'TBI' from non-TBI, or the laterality of the lesion. In addition, the classification accuracy for identifying patients with specific lobe lesions was low, except for the parietal lobe with an accuracy of 63%. CONCLUSION: The CDT is not an accurate tool for detecting focal brain lesions. While the CDT still is beneficial for use with patients suspected of having a neurodegenerative disorder, it should be cautiously used with patients with focal neurological disorders.
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 | 0.000 | 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 | 0.001 |
| 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".