Predicting trajectories of cognitive change in patients with mild cognitive impairment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
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, and their association with readily available clinical information, have not been well defined. Study 1 sought to identify the developmental trajectory of groups with distinct cognitive change patterns among a cohort of MCI patients. Study 2 was conducted to identify individual items/subtests of the Mini-Mental State Examination (MMSE) and demographic variables at baseline that predicted the identified trajectories of cognitive change from Study 1. One hundred and eighty-seven MCI patients were evaluated serially with the MMSE for up to 3.5 years. Five trajectories were identified and labeled based on their baseline MMSE score and course: 29-stable (6.4%); 27-stable (53.9%); 25-slow-decline (23.8%); 24-slow-decline (11.6%); 25-rapid-decline (4.2%). In multivariate logistic regression analysis, a model was established to dissociate patients with stable vs. declining trajectories. An equation derived from this model that included age, delayed recall, constructional praxis, attention, and orientation to time and floor predicted future cognitive decline with good accuracy (79.9%) and specificity (86.4%), and moderate sensitivity (67.2%). The identification of varying trajectories of cognitive change and predictors of cognitive decline from easily obtained baseline clinical information can help target at-risk groups for early interventions aimed at delaying the onset of dementia.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it