Relationships of change in Clinical Dementia Rating (CDR) on patient outcomes and probability of progression: observational analysis
Bibliographic record
Abstract
BACKGROUND: Understanding the relationship among changes in Clinical Dementia Rating (CDR), patient outcomes, and probability of progression is crucial for evaluating the long-term benefits of disease-modifying treatments. We examined associations among changes in Alzheimer's disease (AD) stages and outcomes that are important to patients and their care partners including activities of daily living (ADLs), geriatric depression, neuropsychiatric features, cognitive impairment, and the probabilities of being transitioned to a long-term care facility (i.e., institutionalization). We also estimated the total time spent at each stage and annual transition probabilities in AD. METHODS: The study included participants with unimpaired cognition, mild cognitive impairment (MCI) due to AD, and mild, moderate, and severe AD dementia in the National Alzheimer's Coordinating Center (NACC) Uniform Data Set (UDS) database. The associations among change in AD stages and change in relevant outcomes were estimated using linear mixed models with random intercepts. The probability of transitioning to long-term care facilities was modeled using generalized estimating equations. The total length of time spent at AD stages and annual transition probabilities were estimated with multistate Markov models. RESULTS: The estimated average time spent in each stage was 3.2 years in MCI due to AD and 2.2, 2.0, and 2.8 years for mild, moderate, and severe AD dementia, respectively. The annual probabilities of progressing from MCI to mild, moderate, and severe AD dementia were 20, 4, and 0.7%, respectively. The incremental change to the next stage of participants with unimpaired cognition, MCI, and mild, moderate, and severe AD dementia (to death) was 3.2, 20, 26.6, 31, and 25.3%, respectively. Changes in ADLs, neuropsychiatric features, and cognitive measures were greatest among participants who transitioned from MCI and mild AD dementia to more advanced stages. Participants with MCI and mild and moderate AD dementia had increasing odds of being transitioned to long-term care facilities over time during the follow-up period. CONCLUSIONS: The findings demonstrated that participants with early stages AD (MCI or mild dementia) were associated with the largest changes in clinical scale scores. Early detection, diagnosis, and intervention by disease-modifying therapies are required for delaying AD progression. Additionally, estimates of transition probabilities can inform future studies and health economic modeling.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".