Construct Validity and Test‐Retest Reliability of the Digital Brain Function Screen (DBFS) for Early Cognitive Impairment
Bibliographic record
Abstract
BACKGROUND: The Digital Brain Function Screen (DBFS) is a medical-grade digital cognitive screening tool designed to detect early stages of Mild Cognitive Impairment (MCI) and monitor cognitive decline. While DBFS has shown comparability to the Montreal Cognitive Assessment (MoCA), a gold-standard tool for MCI detection, its construct validity and test-retest reliability had not been formally established. The aim of this study was to evaluate the construct validity and test-retest reliability of DBFS in comparison to MoCA. METHOD: A total of 157 individuals aged 10 to 81 years were recruited from a neurology clinic, with 36 participants completing a second DBFS assessment. Both DBFS and MoCA were administered during the same session, with an average interval between the first and second DBFS administrations of 305.33 ± 354.19 days (range: 1-1090 days). Construct validity was assessed using partial Spearman's rank correlations between domain-specific DBFS and MoCA scores, controlling for covariates including age, sex, and education. Test-retest reliability was evaluated by comparing DBFS scores from the first and second administrations using partial Spearman's rank correlation, while controlling for the same covariates. Statistical analysis was performed using R (https://www.r-project.org/). RESULT: The DBFS total score was significantly correlated with the MoCA total score (p < 0.001), and strong positive correlations (p < 0.05) were observed between DBFS domains and corresponding MoCA domains, indicating good construct validity. Among the 36 participants who completed the DBFS a second time, Spearman's ρ was 0.65 (p < 0.001), demonstrating a strong monotonic relationship and high reliability over time. CONCLUSION: The study confirms that DBFS domains effectively measure the intended cognitive constructs and maintain high reliability across repeated administrations. These findings establish the validity and reliability of DBFS as a practical, scalable, and efficient tool for detecting and monitoring early cognitive impairment. Its usability makes it suitable for deployment in various healthcare settings, including screening centers and specialty clinics.
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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 | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.000 |
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".