<p>Comparative Study of Two Short-Form Versions of the Montreal Cognitive Assessment for Screening of Post-Stroke Cognitive Impairment in a Chinese Population</p>
Bibliographic record
Abstract
PURPOSE: Cognitive impairment (CI) is one of the most significant post-stroke complications. The Montreal Cognitive Assessment (MoCA) is widely applied to the early screening of post-stroke CI (PSCI), and has good sensitivity and specificity, but needs a long time to administer. Clinicians and researchers need shorter, more effective cognitive testing tools. The purpose of this study was to detect the sensitivity and specificity of two different short-form versions of the MoCA (SF-MoCA) for screening of PSCI in a Chinese population. METHODS: A total of 2,989 stroke participants were included from 14 hospitals in northern and southern China between June 2011 and September 2013. The sensitivity and specificity of the two SF-MoCA versions were compared. RESULTS: Using an MoCA score <26 as the critical value, the National Institute of Neurological Disease and Stroke-Canadian Stroke Network SF-MoCA showed sensitivity of 91% and specificity of 63% (PPV 71%, BPV 87%) with scores ≤10 points. The sensitivity and specificity of the Bocti SF-MoCA were 92% and 69% (PPV 75%, BPV 89%) with scores ≤7, respectively. The area under the curve was 0.885 (95% CI 0.873-0.897) and 0.912 (95% CI 0.902-0.922), respectively. CONCLUSION: The Bocti SF-MoCA can be used as a briefer and more effective screening tool for PSCI in Chinese.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".