Identifying and Characterizing Trajectories of Cognitive Change in Older Persons with Mild Cognitive Impairment
Bibliographic record
Abstract
BACKGROUND: Mild cognitive impairment (MCI) represents a state of high risk for dementia but is heterogeneous in its course. To date, the trajectories reflecting distinct developmental courses of cognition among patients with MCI have not been well defined. AIM: To identify the developmental trajectory of groups with distinct cognitive change patterns among a cohort of MCI patients. METHODS: 187 MCI patients from 2 geriatric outpatient clinics were evaluated serially with the Mini-Mental State Examination (MMSE) for up to 3.5 years. Group-based trajectory analysis was applied to identify distinct trajectories. Estimates of decline for each group were compared with the mean rate of decline obtained from mixed modeling of the entire sample. RESULTS: 5 trajectories were identified and labeled based on their baseline MMSE score and course: (1) 29/stable (6.5%); (2) 27/stable (53.9%); (3) 25/slow decline (23.8%); (4) 24/slow decline (11.6%); (5) 25/rapid decline (4.2%). Annual rate of change in the MMSE score for these 5 groups was 0.09, -0.43, -1.23, -1.84, and -4.6 points, respectively. None corresponded to the mean rate of -0.82 points estimated for the group as a whole. A majority of MCI patients (60.4%) follow stable cognitive trajectories over time. Within the 3 groups with declining trajectories, cognitive decline occurs slowly in a vast majority of MCI patients (98.5%). CONCLUSIONS: Results provide direct evidence for the heterogeneous course of cognitive decline that has been suggested by the variable prognosis for patients with MCI.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".