The NINDS-Canadian stroke network vascular cognitive impairment neuropsychology protocols in Chinese
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Vascular cognitive impairment (VCI) affects up to half of stroke survivors and predicts poor outcomes. Valid and reliable assessement for VCI is lacking, especially for the Chinese population. In 2005, the National Institute of Neurological Disorders and Stroke and Canadian Stroke Network (NINDS-CSN) Harmonisation workshop proposed a set of three neuropsychology protocols for VCI evaluation. This paper is to introduce the protocol design and to report the psychometric properties of the Chinese NINDS-CSN VCI protocols. METHODS: Fifty patients with mild stroke (mean National Institute of Health Stroke Scale 2.2 (SD=3.2)) and 50 controls were recruited. The NINDS-CSN VCI protocols were adapted into Chinese. We assessed protocols' (1) external validity, defined by how well the protocol summary scores differentiated patients from controls using receiver operating characteristics (ROC) curve analysis; (2) concurrent validity, by correlations with functional measures including Stroke Impact Scale memory score and Chinese Disability Assessment for Dementia; (3) internal consistency; and (4) ease of administration. RESULTS: All three protocols differentiated patients from controls (area under ROC for the three protocols between 0.77 to 0.79, p<0.001), and significantly correlated with the functional measures (Pearson r ranged from 0.37 to 0.51). A cut-off of 19/20 on MMSE identified only one-tenth of patients classified as impaired on the 5-min protocol. Cronbach's α across the four cognitive domains of the 60-min protocol was 0.78 for all subjects and 0.76 for stroke patients. CONCLUSIONS: The Chinese NINDS-CSN VCI protocols are valid and reliable for cognitive assessment in Chinese patients with mild stroke.
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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 | 0.052 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".