Comparative Study of Two Short-Form Versions of the Montreal Cognitive Assessment for Screening of Post-Stroke Cognitive Impairment in a Chinese Population
Bibliographic record
Abstract
Jingjing Wei,1– 3,* Xianglan Jin,2,* Baoxin Chen,2 Xuemei Liu,4 Hong Zheng,4 Rongjuan Guo,2 Xiao Liang,3 Chen Fu,2,4 Yunling Zhang2,3 1Beijing University of Chinese Medicine, Beijing, People’s Republic of China; 2Department of Neurology, Dongfang Hospital, Beijing University of Chinese Medicine, Beijing, People’s Republic of China; 3Department of Neurology, Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, People’s Republic of China; 4Central Laboratory, Dongfang Hospital, Beijing University of Chinese Medicine, Beijing, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yunling ZhangDepartment of Neurology, Xiyuan Hospital, China Academy of Chinese Medical Sciences, 1 Xiyuan Playground, Haidian District, Beijing 100091, People’s Republic of ChinaTel/Fax +86-10-6283-5960Email yunlingzhang2004@126.comChen FuCentral Laboratory, Dongfang Hospital, Beijing University of Chinese Medicine, 6 First Block, Fangxingyuan, Fengtai District, Beijing 100078, People’s Republic of ChinaTel +86-10-6768-9634Fax +86-10-67691949Email fuchen2003@163.comPurpose: 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.Keywords: cognitive dysfunction, Montreal Cognitive Assessment, MoCA, stroke, sensitivity, specificity
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".