Longitudinal Patterns and Predictors of Cognitive Impairment Classification Stability
Bibliographic record
Abstract
OBJECTIVE: Classifications such as Cognitive Impairment, No Dementia (CIND) are thought to represent the transitory, pre-clinical phase of dementia. However, increasing research demonstrates that CIND represents a nonlinear, unstable entity that does not always lead to imminent dementia. The present study utilizes a longitudinal repeated measures design to gain a thorough understanding of CIND classification stability patterns and identify predictors of future stability. The objectives were to i) explore patterns of longitudinal stability in cognitive status across multiple assessments and ii) investigate whether select baseline variables could predict 6-year CIND stability patterns. METHOD: Participants (N = 259) included older adults (aged 65-90 years) from Project MIND, a six-year longitudinal repeated measures design in which participants were classified as either normal cognition (NC) or CIND at each annual assessment. A latent transition analysis approach was adapted in order to identify and characterize transitions in CIND status across annual assessments. Participants were classified as either Stable NC, Stable CIND, Progressers, Reverters, or Fluctuaters. Multinomial logistic regression was employed to test whether baseline predictors were associated with cognitive status stability patterns. RESULTS: The sample demonstrated high rates of reversion and fluctuation in CIND status across annual assessments. Additionally, premorbid IQ and CIND severity (i.e., single vs. multi-domain impairment) at baseline were significantly associated with select stability outcomes. CONCLUSIONS: CIND status was unstable for several years following baseline assessment and cognitive reserve may delay or protect against demonstrable cognitive impairment. Further, consideration of cognitive impairment severity at the time of initial classification may improve CIND classifications.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".