Five-Year Trajectory of Mild Cognitive Impairment: Insights from a Primary Care Memory Clinic
Bibliographic record
Abstract
BACKGROUND: The trajectory of Mild Cognitive Impairment (MCI) to dementia within primary care is not well understood. OBJECTIVE: We investigated the 5-year trajectory of patients initially diagnosed with MCI, evaluated their risk of developing dementia considering age, sex, and Montreal Cognitive Assessment (MoCA) test scores and determined the annual conversion rate from MCI to dementia for patients assessed in a MINT (Multispecialty Interprofessional Team) memory clinic. METHODS: We conducted a longitudinal cohort study using a retrospective chart review of 751 patients assessed within a MINT memory clinic in Ontario, Canada. The conversion rate from MCI to dementia was estimated with the Kaplan-Meier method. Cox regression examined time to dementia diagnosis and the association between baseline MoCA scores and dementia risk. FINDINGS: The observed 5-year conversion rate from MCI to dementia was 28.0%, though with limited follow-up data. Accounting for missing data, the estimated 5-year conversion rate was 48.8% (39.5%, 59.2%) with an average annual rate of 9.8%. Each one-point increase in MoCA score at initial visit was associated with a 10% lower rate of conversion to dementia (aHR: 0.90, 95%CI: 0.85-0.96). DISCUSSION: Findings highlight the profile of patients assessed in MINT clinics, cognitive trajectory of those diagnosed with MCI, and the importance of primary care-based memory clinics in early detection and intervention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".