CLINICAL EFFICACY OF A DIGITAL COGNITIVE ASSESSMENTS IN DETECTING COGNITIVE IMPAIRMENTS: A POOLED ANALYSIS
Bibliographic record
Abstract
Abstract Dementia has become a public health concern worldwide with significant social and economic impacts. Early detection of dementia, which involves diagnosis of mild cognitive impairment (MCI), a possible prestage of dementia, is crucial for timely interventions, care planning, and reducing healthcare costs. To promote proactive screening, it is important to equip primary care providers with assessment tools that are not only validated and reliable but also easy-to-administer and seamlessly integrated into their routine workflows. BrainCheck’s cognitive assessment tool (BC-Assess), with its demonstrated high diagnostic efficacy, usability, and feasibility of implementation, offers a promising solution. This study aimed to re-evaluate the efficacy of BC-Assess in identifying MCI and dementia based on a pooled dataset. The dataset included BC-Assess data of 193 individuals (age mean=73.7; SD=11.2), comprising those with normal cognition (NC; N=48) and those diagnosed with MCI (N=53) or dementia (N=92), acquired from two previous studies and from BrainCheck’s clinical partners. Each individual’s cognitive status was determined based on results from provider clinical diagnoses (N=158; 82%) or from the Montreal Cognitive Assessment (N=35; 18%). Significant differences in BC-Assess overall scores were found between the three groups (p<.001). ROC analysis shows that BC-Assess can achieve high sensitivity (>=85%) and specificity (>=81%) in distinguishing MCI and dementia patients from those with normal cognition, comparable with performance level obtained previously from a single data source. These results demonstrate the ability of BC-Assess to hold its diagnostic efficacy over combined data collected from different 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 | 0.048 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.017 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".