Validation of the Clock Drawing Test Scoring Method in older adults with neurocognitive disorder
Bibliographic record
Abstract
Introduction. The Clock Drawing Test (CDT) is a widely used instrument for identifying neurocognitive disorders (NCDs) in older adults. However, there is insufficient evidence to determine the best scoring method, since current quantitative methods involve the assignment of numerical values, while qualitative ones do not allow for objectivity in the diagnosis. Parsey and Schmitter-Edgecombe (2011) proposed a scoring scheme which, in addition to providing a score of the patient’s performance, permits error analysis, thereby making it possible to identify potential underlying cognitive difficulties. Objective. The purpose of this study was to validate the CDT scoring scheme proposed by Parsey and Schmitter-Edgecombe (2011) for screening for NCDs in Mexican older adults. Method. There were 167 participants: 58 cognitively healthy subjects (CH), 52 with mild neurocognitive disorder (mild-NCD), and 57 with major neurocognitive disorder (major-NCD).The CDT scoring method was compared with the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment in Spanish (MoCA-S). Inter- and intra-observer reliability and construct validity were determined and the sensitivity and specificity of this method were calculated. Results. The age was 75 years (SD ± 8 years) and the educational attainment was 10.7 years (SD ± 5.2 years). Internal reliability was .750, with an intraclass correlation coefficient of .774. The cut-off point for the CDT in mild-NCD was 14 points (sensitivity: 40%, specificity: 70%) and 12 points for major-NCD (sensitivity: 90%, specificity: 95%).The most frequent errors in the CDT were: graphic, conceptual, spatial, and/or planning difficulties. Discussion and conclusion. This method makes it possibly to quickly and easily explore the cognitive status of the patient. It contains ideal psychometric properties for the detection of patients with major-NCD, in addition to offering the possibility of analyzing performance errors and underlying cognitive difficulties.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
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".