Development and validation of the DCOS (Digitalised Combined Objective‐Subjective cognitive screening): A five‐minute neuropsychological test for mild neurocognitive disorder
Bibliographic record
Abstract
BACKGROUND: Early detection of mild neurocognitive disorder (NCD) enables timely and targeted interventions, especially before progression to major neurocognitive disorder. This study aims to develop a sensitive and simple digitalised screening tool, using artificial intelligence to capture and interpret spoken language, for the detection of mild NCD. METHOD: The Digitalised Combined Objective-Subjective (DCOS) cognitive screening tool was developed based on DSM-5 criteria, which includes subjective and objective cognitive impairment for the diagnosis of mild NCD. We developed and validated on two independent cohorts: a dementia speech biomarkers cohort (n = 977) for development and the Screening for Early Alzheimer's Disease Study cohort (n = 127) for validation. Mild NCD is defined using the Hong Kong List Learning Test (HKLLT with below 1SD cutoff; see Table 1). DCOS items were selected through ROC analysis from existing validated cognitive assessments, incorporating both subjective and objective domains, with the criteria that the items must be assessed using spoken language for the development of the digitalised tool. The scoring weights were calculated according to the estimated coefficients of a multivariate logistic regression model. RESULT: The new tool DCOS comprises six items (three subjective, three objective) with a maximum score of 16 points. In the development cohort, DCOS demonstrated good diagnostic accuracy (AUC=0.75) comparable to Hong Kong Montreal Cognitive Assessment-5min (MoCA-5min) (AUC=0.77) and superior to Ascertain Dementia 8 (AD8) (AUC=0.58). At the optimal cutoff score of 9.5, DCOS achieved a better balanced performance (F1=0.56) than both HK MoCA-5min (F1=0.48) and AD8 (F1=0.44). External validation confirmed robust performance with improved diagnostic accuracy (AUC=0.87), achieving 88% sensitivity and 69% specificity (see Table 2). The sampling of the items and and the nature of the test has shown the feasibility of developing into a digitalised test. CONCLUSION: DCOS is a novel screening tool that assesses cognitive impairment from both subjective and objective domains. It presents a potential discriminative performance for mild NCD detection. The brief administration time (<5 minutes) makes it particularly suitable for primary health care. As a next step, DCOS will be incorporated with artificial intelligence and be applied on a digital platform to enhance its utility in large-scale settings.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".