A - 18Stability of MoCA Scores for Patients Seen in a Memory Disorders Clinic
Bibliographic record
Abstract
Objective: To determine the rate of change in Montreal Cognitive Assessment (MoCA) scores over time among patients evaluated in a memory disorders clinic. Method: At each visit, a physician assigned the following diagnoses based on clinical evaluation; cognitively intact (CI); cognitive dysfunction- not neurodegenerative (CD); Mild cognitive impairment (MCI), Alzheimer’s disease (AD), and other dementia (FTD, DLB, PPA). Of 1632 patients included in this study, 470 patients completed 2 or more MoCA’s with same diagnosis over a 6-year period. Results: Of the 339 patients with 823 visits, 13 remained classified as cognitively intact, 41 as cognitive dysfunction (did not meet criteria for MCI), 180 MCI, 45 AD, and 60 classified as other dementia. Average age 68.0 (SD = 8.5) and 69% had more than 12 years of education. There were significant group differences in age and baseline MoCA score. The average time between MoCA completions was 390.6 days (SD = 273.7). Results of the mixed-effects linear regression model showed about 1 point/year change in patients with MCI (estimate = −0.95, P = 0.04) as well as patients in “other dementia” category (estimate = −1.08, P = 0.039) compared to those cognitively intact. Patients with AD declined 2.19 points/year compared to the cognitively intact (P < 0.001). Age and education were not significantly related to MoCA score. Conclusions: MoCA Total scores showed significant decline in older adults with cognitive impairment over a period of 1–4 years but not in the cognitively intact. Results provide clinically relevant information regarding expected change in MoCA scores over time.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".