Does the Cognitive Change Index Predict Future Cognitive and Clinical Decline? Longitudinal Analysis in a Demographically Diverse Cohort
Bibliographic record
Abstract
Background: The Cognitive Change Index (CCI) is a widely-used measure of self-perceived cognitive ability and change. Unfortunately, it is unclear if the CCI predicts future cognitive and clinical decline. Objective: We evaluated baseline CCI to predict transition from normal cognition to cognitive impairment in nondemented older adults and in predementia groups including, subjective cognitive decline, motoric cognitive risk syndrome, and mild cognitive impairment. Different versions of the CCI were assessed to uncover any differential risk sensitivity. We also examined the effect of ethnicity/race on CCI. Methods: Einstein Aging Study participants (N = 322, Mage = 77.57±4.96, % female=67.1, Meducation = 15.06±3.54, % non-Hispanic white = 46.3) completed an expanded 40-item CCI version (CCI-40) and neuropsychological evaluation (including Clinical Dementia Rating Scale [CDR], Montreal Cognitive Assessment, and Craft Story) at baseline and annual follow-up (Mfollow - up=3.4 years). CCI-40 includes the original 20 items (CCI-20) and the first 12 memory items (CCI-12). Linear mixed effects models (LME) and generalized LME assessed the association of CCI total scores at baseline with rate of decline in neuropsychological tests and CDR. Results: In the overall sample and across predementia groups, the CCI was associated with rate of change in log odds on CDR, with higher CCI at baseline predicting faster increase in the odds of being impaired on CDR. The predictive validity of the CCI broadly held across versions (CCI-12, 20, 40) and ethnic/racial groups (non-Hispanic black and white). Conclusions: Self-perception of cognitive change on the CCI is a useful marker of dementia risk in demographically/clinically diverse nondemented samples. All CCI versions successfully predicted decline.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".