Testing the Clinical Dementia Rating Sum of Boxes as an Outcome for Dementia with Lewy Bodies Clinical Trials
Bibliographic record
Abstract
INTRODUCTION: Dementia with Lewy bodies (DLB), a common cause of dementia, has no FDA-approved therapies, and clinical trials to date have had limited ability to demonstrate efficacy. The lack of validated DLB-specific clinical trial outcomes may hinder these efforts. Here, we test whether the Clinical Dementia Rating (CDR) and other commonly used clinical evaluation tools for Alzheimer's disease (AD) and Parkinson's disease (PD) could potentially be used as outcome measures in future DLB clinical trials. METHODS: A retrospective, cross-sectional chart review of 600 patients (359 AD, 241 DLB) who completed a comprehensive clinical, cognitive, functional, and behavioral evaluation over a 10-year period was carried out. Performance of the CDR, its sum of boxes (CDR-SB), and other AD and PD evaluation measures were assessed for stage-wide performance from mild cognitive impairment (CDR 0.5) to moderate-severe dementia (CDR 2). RESULTS: The CDR and CDR-SB characterize important differences between AD and DLB across different cross-sectional stages of disease severity, with the greatest differences seen at the CDR 0.5 stage. DLB showed greater deficits in commonly used AD functional and behavioral measures at the CDR 0.5 stage, while more DLB-specific measures showed significant differences from AD across the entire disease spectrum. The patient version of the Quick Dementia Rating System showed greater stage-wide impairment in DLB than AD, supporting its use as a patient-reported outcome. The Montreal Cognitive Assessment showed greater stage-wide impairment in AD than in DLB patients, suggesting lack of sensitivity as an outcome measure for DLB clinical trials. CONCLUSION: Improved study design and selection of appropriate outcome measures in DLB clinical trials can facilitate demonstration of efficacy. While the CDR-SB could work on a DLB clinical trial, the field would be most advanced by the development of a DLB-specific global rating instrument.
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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 | 0.095 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".