Comparative Performance of the Digital Clock and Recall™ Test, Montreal Cognitive Assessment, and Saint Louis University Mental Status Among Patients in Primary Care
Bibliographic record
Abstract
BACKGROUND: Digital cognitive assessment solutions can overcome some barriers to cognitive screening in primary care by providing rapidly-obtained objective insights without requiring specialty-trained examiners. The Linus Health Digital Clock and Recall (DCR™) is a three-part test consisting of three-word immediate verbal acquisition, the Digital Clock Test (DCTclock™), and delayed recall of the three words. Our objective was to compare performance on the DCR to the Montreal Cognitive Assessment (MoCA; Nasreddine et al., 2005) and the Saint Louis University Mental Status (SLUMS; Tariq et al., 2006), two commonly used screening tests in primary care. METHOD: Comparative analyses of DCR, MoCA (Total Score and Memory Index Score [MIS]), and SLUMS results were conducted among 80 primary care patients - 65 years and older presenting for any reason - whose DCR performance was indicative of cognitive impairment. RESULT: Of the 80 patients (mean age 74.0 [±7.8]; 60% female) performing as impaired or borderline impaired on the DCR, 67 completed the MoCA and 13 completed the SLUMS. Comparison between the DCR and MoCA Total Score included 78% (52/67) concordance for identifying impairment (cutoff <26). While the remaining 15 of 67 patients (22%) had normal MoCA Total Scores (≥26), 13 (87%) missed points in at least one MoCA cognitive domain and 8 (53%) missed points in multiple MoCA domains (often language and delayed recall). Comparison between DCR and SLUMS revealed that all 13 patients receiving SLUMS assessment had DCR scores 0-1 and SLUMS scores <27 (indicating cognitive impairment). See Tables 1 and 2 for breakdown of MoCA, MIS, and SLUMS stratified by DCR performance. Logistic regression found that DCTclock metrics of information processing (z = 2.00, p = .045) and spatial reasoning (z = 2.82 p = .005) discriminated cognitive impairment on MoCA Total Score (cutoff <26). Overall, ROC analysis revealed good accuracy of DCR to identify MoCA performance (AUC = .84; Figure 1). CONCLUSION: Screening in primary care using the DCR is feasible, takes less time to administer than MoCA or SLUMS, and shows concordant results with these more established screening tools in detecting cognitive impairment. Future research aims to investigate the criterion validity of the DCR among biomarkers for neurodegenerative disease.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".