Predicting decline in mild cognitive impairment: A prospective cognitive study.
Bibliographic record
Abstract
OBJECTIVE: The primary aim of this study was to identify cognitive tests that differentiate between persons with mild cognitive impairment (MCI) who later develop cognitive decline and those who remain stable. METHOD: This study used a prospective longitudinal design. One hundred twenty-two older adults with single-domain or multiple-domain amnestic MCI were recruited from memory clinics. They completed tests to measure baseline episodic memory, working memory, executive functions, perception, and language. They were then followed annually to determine with criteria independent from those tests whether they had remained stable or had developed dementia or significant cognitive decline. This was used as the reference standard to measure diagnostic test accuracy value. RESULTS: ANOVAs indicated that participants with progressive MCI showed more impaired performance than those with stable MCI at baseline on episodic memory (word and story recall), the Brown-Peterson working memory test, object naming, object decision, and position of gap test. Logistic regression derived a significant model with 87.8% overall predictive value. The model included delayed text memory, free recall, naming, orientation match, object decision, and alpha span. Its sensitivity was 86.2% and its specificity was 88.9%. Positive predictive value was 83.3%, and negative predictive value was particularly high at 90.9%. CONCLUSIONS: Identifying individuals with MCI who will progress to dementia or more severe cognitive impairment is a challenge. This study shows that cognitive measures provide valuable information regarding the predictive diagnosis of persons with MCI. Predictive accuracy of a cognitive battery might be optimized by selecting both memory and nonmemory measures.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".