Screening for Cognitive Impairment After Stroke: Validation of the Chinese Version of the Quick Mild Cognitive Impairment Screen
Bibliographic record
Abstract
Background: Screening for post-stroke cognitive impairment (PSCI) is necessary because stroke increases the incidence of and accelerates premorbid cognitive decline. The Quick Mild Cognitive Impairment (Q mci ) screen is a short, reliable and accurate cognitive screening instrument but is not yet validated in PSCI. We compared the diagnostic accuracy of a Chinese version of the Q mci screen (Q mci -CN) compared with the widely-used Chinese versions of the Montreal Cognitive Assessment (MoCA-CN) and Mini-Mental State Examination (MMSE-CN). Methods: We recruited 34 patients who had recovered from a stroke in rehabilitation unit clinics in 2 university hospitals in China: 11 with post-stroke dementia (PSD), 15 with post-stroke cognitive impairment no dementia (PSCIND), and 8 with normal cognition (NC). Classification was made based on clinician assessment supported by a neuropsychological battery, independent of the screening test scores. The Q mci -CN, MoCA-CN, and MMSE-CN screens were administered randomly by a trained rater, blind to the diagnosis. Results: The mean age of the sample was 63 ± 13 years and 61.8% were male. The Q mci -CN had statistically similar diagnostic accuracy in differentiating PSD from NC, an area under the curve (AUC) of 0.94 compared to 0.99 for the MoCA-CN ( p = 0.237) and 0.99 for the MMSE-CN ( p = 0.293). The Q mci -CN (AUC 0.91), MoCA-CN (AUC 0.94), and MMSE-CN (AUC 0.79) also had statistically similar accuracy in separating PSD from PSCIND. The MoCA-CN more accurately distinguished between PSCIND and normal cognition than the Q mci -CN ( p = 0.015). Compared to the MoCA-CN, the administration times of the Q mci -CN (329s vs. 611s, respectively, p < 0.0001) and MMSE-CN (280 vs. 611s, respectively, p < 0.0001) were significantly shorter. Conclusion: The Q mci -CN is accurate in identifying PSD and separating PSD from PSCIND in patients post-stroke following rehabilitation and is comparable to the widely-used MoCA-CN, albeit with a significantly shorter administration time. The Q mci -CN had relatively poor accuracy in identifying PSCIND from NC and hence may lack accuracy for certain subgroups. However, given the small sample size, the study is under-powered to show superiority of one instrument over another. Further study is needed to confirm these findings in a larger sample size and in other settings (countries and languages).
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".