Comparing the accuracy of the DCTclock and Montreal Cognitive Assessment to detect cognitive impairment and cerebral amyloid status in older adults
Bibliographic record
Abstract
Abstract Background Sensitive and non‐invasive methods of screening for early‐stage Alzheimer’s disease (AD) are urgently needed. Digital assessment tools have the potential to improve the efficiency of cognitive screening for older adults in both clinical and research settings. The Linus Health DCTclock uses a digital pen to capture traditional clock drawing test performance and advanced analytics to evaluate the drawing process for indicators of cognitive difficulty. Method We compared the DCTclock to the Montreal Cognitive Assessment (MoCA), a standard cognitive screening test, in a sample of older adults (total N = 60) with normal cognition (n = 30) or a clinical diagnosis of mild cognitive impairment (MCI) or AD (n = 30) and investigated which measure is more accurate in predicting cerebral amyloid (Aβ) PET status in a subset of 32 participants with PET imaging data. Result MoCA total score was moderately correlated with DCTclock total score (r = 0.61, p < 0.01), as well as various DCTclock composite sub‐scores. ROC analysis indicated that the MoCA had superior accuracy in differentiating between cognitively impaired and unimpaired participants (AUC = 0.98) relative to the DCTclock (AUC = 0.82). ROC analysis also indicated that the MoCA had superior accuracy in differentiating elevated versus non‐elevated Aβ PET status (AUC = 0.76) relative to the DCTclock (AUC = 0.60). A composite of the MoCA and DCTclock total scores did not improve accuracy over the MoCA alone (AUC = 0.71) Conclusion Overall, these preliminary results suggest that the MoCA is a superior cognitive screening tool and may also be useful for detecting AD associated neuropathology.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".