Longitudinal Trajectories of Informant-Reported Daily Functioning in Empirically Defined Subtypes of Mild Cognitive Impairment
Bibliographic record
Abstract
OBJECTIVES: Within the Alzheimer's Disease Neuroimaging Initiative (ADNI)'s mild cognitive impairment (MCI) cohort, we previously identified MCI subtypes as well as participants initially diagnosed with MCI but found to have normal neuropsychological, biomarker, and neuroimaging profiles. We investigated the functional change over time in these empirically derived MCI subgroups. METHODS: ADNI MCI participants (n=654) were classified using cluster analysis as Amnestic MCI (single-domain memory impairment), Dysnomic MCI (memory+language impairments), Dysexecutive/Mixed MCI (memory+language+attention/executive impairments), or Cluster-Derived Normal (CDN). Robust normal control participants (NCs; n=284) were also examined. The Functional Activities Questionnaire (FAQ) was administered at baseline through 48-month follow-up. Multilevel modeling examined FAQ trajectories by cognitive subgroup. RESULTS: The Dysexecutive/Mixed group demonstrated the fastest rate of decline across all groups. Amnestic and Dysnomic groups showed steeper rates of decline than CDNs. While CDNs had more functional difficulty than NCs across visits, both groups' mean FAQ scores remained below its suggested cutoff at all visits. CONCLUSIONS: Results (a) show the importance of executive dysfunction in the context of other impaired cognitive domains when predicting functional decline in at-risk elders, and (b) support our previous work demonstrating that ADNI's MCI criteria may have resulted in false-positive MCI diagnoses, given the CDN's better FAQ trajectory than those of the cognitively impaired MCI groups. (JINS, 2017, 23, 521-527).
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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 | 0.006 |
| 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 | 0.001 |
| 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".