Mild cognitive impairment is associated with selected functional markers: Integrating concurrent, longitudinal, and stability effects.
Bibliographic record
Abstract
OBJECTIVE: We examined functional performance on multiple indicators for two cognitive status groups: (a) not impaired controls (NIC) and (b) mild cognitive impairment (MCI). We identified functional markers associated with differences, changes, and stability in cognitive status. METHOD: In the Victoria Longitudinal Study (VLS) we examined cognitive status group effects in (a) cross-sectional functional performance, (b) longitudinal stability, (c) longitudinal functional performance change, and (d) functional marker prediction of later cognitive status. We assembled markers from five continuous clusters of MCI-related functional factors: biological vitality, activity lifestyle, psychosocial affect, subjective health, and global cognition. We used a cross-sectional sample and a two-wave longitudinal sample, stratified by age (mid-old, old-old) and cognitive status (MCI, NIC). RESULTS: First, cross-sectional results showed that eight markers differentiated MCI and NIC adults, with the latter performing uniformly better. The groups differed on diastolic blood pressure, body mass index, positive and negative affect, MMSE, and the lifestyle indicators of self-maintenance, travel, and novel cognitive activities. Second, Wave 1 to Wave 2 stabilities in cognitive status classification were high. Third, several markers differentiated the stable (NIC-to-NIC, MCI-to-MCI) from the unstable (NIC-to-MCI, MCI-to-NIC) cognitive status groups. Fourth, five relevant markers for identifying older adults at risk for cognitive status changes were: diastolic blood pressure, self-maintenance activities, novel cognitive activities, positive affect, and global cognitive status. CONCLUSION: Selected risk and protective factors differentiate persons classified with MCI from those not currently cognitively impaired, both cross-sectionally and longitudinally.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".