Transition from Cognitively Impaired Not Demented to Alzheimer’s Disease: An Analysis of Changes in Functional Abilities in a Dementia Clinic Cohort
Bibliographic record
Abstract
BACKGROUND: Patients with cognitive impairment no dementia (CIND) are at an increased risk of progression to Alzheimer's disease (AD). Whether subtle impairments in functional or social abilities at the CIND stage can predict progression to AD is not yet fully determined. We evaluated whether impairments on the Disability Assessment for Dementia (DAD) and Functional Rating Scale (FRS) can predict progression to AD. METHODS: We examined 70 patients with CIND from the ACCORD cohort having complete DAD and FRS baseline and 2-year follow-up data. MANCOVA adjusted for age, sex, education and baseline MMSE score compared the baseline and 2-year change in DAD and FRS scores in CIND patients who progressed to AD versus non-progressors. RESULTS: There were no significant differences between CIND progressors and non-progressors in baseline total DAD or FRS scores. FRS domain analysis revealed that greater impairment in social/occupational functioning significantly predicted progression, while there were no predictive DAD domains. In progressors, both DAD and FRS scores significantly declined over time with the largest changes in instrumental activities of daily living (IADL). CONCLUSION: While changes in IADL characterize the progression from CIND to AD, impairment in complex social-cognitive competency significantly predicts risk of progression and may mark early AD.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".