Screening for cognitive impairment with the Montreal Cognitive Assessment in Chinese patients with acute mild stroke and transient ischaemic attack: a validation study
Bibliographic record
Abstract
OBJECTIVE: We aimed to establish the cut-off point of the Montreal Cognitive Assessment (MoCA-Beijing) in screening for cognitive impairment (CI) within 2 weeks of mild stroke or transient ischaemic attack (TIA). METHODS: A total of 80 acute mild ischaemic stroke patients and 22 TIA patients were recruited. They received the MoCA-Beijing and a formal neuropsychological test battery. CI was defined by 1.5 SD below the established norms on a formal neuropsychological test battery. RESULTS: Most stroke and TIA patients were in their 50s (53.95±11.43 years old), with greater than primary school level of education. The optimal cut-off point for MoCA-Beijing in discriminating patients with CI from those with no cognitive impairment (NCI) was 22/23 (sensitivity 85%, specificity 88%, positive predictive value=91%, negative predictive value=80%, classification accuracy=86%). The predominant cognitive deficits were characteristic of frontal-subcortical impairment, such as visuomotor speed (46.08%), attention/executive function (42.16%) and visuospatial ability (40.20%). CONCLUSIONS: A MoCA-Beijing cut-off score of 22/23 is optimally sensitive and specific for detecting CI after mild stroke, and TIA in the acute stroke phase, and is recommended 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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".