89Comparison Study of The Montreal Cognitive Assessment(MoCA-CN) and The Quick Mild Cognitive Impairment Screen (Qmci-CN) in Post-Stroke Patients
Bibliographic record
Abstract
Background: Post-stroke cognitive impairment (PSCI) can be divided into post-stroke cognitive impairment no dementia (PSCIND) and post-stroke dementia (PSD) according to the degree of the cognitive decline. Several cognitive screening instruments are available but there is no evidence to support which to use in clinical practice. Further, PSCI is often not detected. Given this, it is necessary to screen for PSCI. The most commonly used instruments to assess for PSCI are the Montreal Cognitive Assessment (MoCA-CN) and the Mini-Mental State Examination (MMSE-CN). The Quick Mild Cognitive Impairment screen (Qmci-CN) is a short cognitive screen that has yet to be validated in stroke. Methods: We recruited post-stroke patients from a rehabilitation unit in a large university hospital; 11 with PSD, 15 with PSCIND, 10 with normal cognition (NC). The Qmci-CN, MoCA-CN and MMSE-CN were administered by the trained rater, blind to the diagnosis. Results: In total, 36 patients were available; 61% (22/36) were female. The median age of patients was 63 (interquartile +/−13) years and the median years in education was 12 (+/−4). The median Qmci-CN screen score was 49/100 (+/−16), the median MMSE was 23/30(+/−7), and the median MoCA-CN score was 20/30(+/−7). In this sample the Qmci-CN was less accurate compared to the MMSE-CN and MoCA-CN in discriminating PSCIND from NC, area under the curve (AUC), 0.551 compared to 0.676 and 0.881, respectively. It has similar accuracy in discriminating PSD from NC, (AUC of 0.924 vs 0.979 and 1.0), and in discriminating PSCIND from PSD, (AUC of 0.894 vs 0.854 and 0.909, respectively). Conclusion: This study suggested that the Qmci-CN has comparable accuracy to the MMSE and MoCA-CN in discriminating PSD but was less accurate in separating NC from PSCIND compared to the MoCA-CN. Further study with a larger sample is needed to confirm these findings.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".