Screening for cognitive impairment with the montreal cognitive assessment at six months after stroke and transient ischemic attack
Bibliographic record
Abstract
OBJECTIVE: Cognitive impairment usually occurs in the acute phase after stroke, but most stroke survivors experience some form of long-term cognitive deficit. The aim of this study was to establish the cutoff point of the Montreal Cognitive Assessment (MoCA-Beijing) in screening for cognitive impairment (CI) at 6 months of ischemic stroke or transient ischemic attack (TIA). METHODS: A total of 301 stroke patients and 15 TIA patients were recruited. Patients were assessed at six months by the MoCA-Beijing and a formal neuropsychological battery. The 1.5 SD below the level of the norm on several tests indicated cognitive impairment (CI). RESULTS: Most stroke and TIA patients were in their 60s (61.23 ± 10.60 years old). The optimal cutoff point for MoCA-Beijing in discriminating patients with CI from those with no cognitive impairment (NCI) was 24/25 (sensitivity 63.28%, specificity 71.22%, PPV = 73.68%, NPV = 60.37%, classification accuracy = 66.72%). The predominant cognitive deficits were visuospatial ability (84.85%), and then attention/executive function (79.27%). CONCLUSION: The MoCA-Beijing cutoff score for differentiating CI from NCI after stroke and TIA at six months was at 24/25, and it is important for routine clinical practice.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".