Decline in changing Montreal Cognitive Assessment (MoCA) scores is associated with post-stroke cognitive decline determined by a formal neuropsychological evaluation
Bibliographic record
Abstract
OBJECTIVES: We aimed to examine changes in the Montreal Cognitive Assessment (MoCA) and Mini-Mental State Examination (MMSE) scores within a one-year period after stroke/transient ischemic attack (TIA) in associating cognitive decline determined by a formal neuropsychological test battery. METHODS: Patients with ischemic stroke/TIA received MoCA and MMSE at baseline within 14 days after stroke/TIA, at 3-6 months and 1-year follow-ups. The scores of MoCA and MMSE were considered to have declined if there were a reduction of ≥2 points in the respective scores measured across two time points. The decline in neuropsychological diagnosis transitional status was defined by a category transition from no cognitive impairment or any cognitive impairment to a more severe cognitive impairment or dementia. RESULTS: 275 patients with a mean age of 59.8 ± 11.6 years, and education of 7.7 ± 4.3 years completed all the assessments at baseline, 3-6 months and 1-year follow-ups. A decline in MoCA scores from 3-6 months to 1 year was associated with higher risk of decline in diagnosis transitional status (odd ratio = 3.21, p = 0.004) in the same time period whereas there was no association with a decline in MMSE scores. CONCLUSIONS: The decline in MoCA scores from 3-6 months to 1 year after stroke/TIA has three times higher risk for decline in the diagnosis transitional status. The decline of MoCA scores (reduction ≥ 2points) is associated with the decline in neuropsychological diagnosis transitional status.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".