Bridging the gap: A conversion framework for CDR-SB and MoCA scores in Alzheimer's disease and related dementia
Bibliographic record
Abstract
BACKGROUND: Accurate assessment of cognitive impairment is essential to effective Alzheimer's disease (AD) management and research. However, the absence of validated methods to translate scores between widely used instruments-such as the Clinical Dementia Rating Scale Sum of Boxes (CDR-SB) in trials and the Montreal Cognitive Assessment (MoCA) in clinical practice-poses a significant barrier. This limits data harmonization, impedes cross-study comparability, and complicates the integration of clinical and research evidence. Bridging this gap is critical for consistent staging, longitudinal monitoring, and data-driven decision-making in AD and related dementias. OBJECTIVES: To develop and validate bidirectional score conversion tables between CDR-SB and MoCA using a large, diverse cohort spanning the full spectrum of cognitive function. DESIGN: Retrospective, cross-sectional analysis using equipercentile equating with log-linear smoothing. Optimal smoothing parameters were selected by minimizing mean squared error, Akaike Information Criterion, and Bayesian Information Criterion. Concordance was assessed using Spearman's rank correlation and Bland-Altman plots. SETTING: National Alzheimer's Coordinating Center (NACC), aggregating standardized assessments from 35 U.S.-based Alzheimer's Disease Research Centers. PARTICIPANTS: 23,717 individuals (59,871 visits) with same-day CDR-SB and MoCA assessments from January 2015 to September 2024, spanning normal cognition, mild cognitive impairment (MCI), and dementia. INTERVENTION: None; this was a secondary analysis of existing data. MEASUREMENTS: Primary measures included CDR-SB (0-18; higher = greater impairment) and MoCA (0-30; higher = better cognition). Bidirectional crosswalk tables were derived using equipercentile equating. RESULTS: CDR-SB and MoCA scores showed strong inverse correlation (Spearman's ρ = -0.68; p < 0.001). Crosswalk tables demonstrated good agreement across the cognitive spectrum and performed consistently in the full cohort and an AD-specific subgroup. CONCLUSIONS: This study provides the first validated, bidirectional CDR-SB-MoCA crosswalk, supporting data harmonization and consistent interpretation of cognitive severity across research and clinical 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 | 0.128 | 0.241 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".