Improving screening for vascular cognitive impairment at three to six months after mild ischemic stroke and transient ischemic attack
Bibliographic record
Abstract
BACKGROUND: The Montreal Cognitive Assessment (MoCA) and Mini-Mental State Examination (MMSE) were compared with and without the addition of a brief processing speed test, the symbol digit modalities test (SDMT), for vascular cognitive impairment (VCI) screening at three to six months after stroke. METHODS: Patients with ischemic stroke and transient ischemic attack were assessed with MoCA and MMSE, as well as a formal neuropsychological battery three to six months after stroke. VCI was defined by impairment in any cognitive domain on neuropsychological testing. The area under the receiver operating characteristic curve (AUC) was used to compare test discriminatory ability. RESULTS: One hundred and eighty-nine patients out of 327 (58%) had VCI, of whom 180 (95%) had vascular mild cognitive impairment (VaMCI), and nine (5%) had dementia. The overall AUCs of the MoCA and MMSE scores and performance at their respective cut-off points were equivalent in detecting VCI (AUCs: 0.87 (95% CI 0.83-0.91) vs. 0.84 (95% CI 0.80-0.88), p = 0.13; cut-offs: MoCA (≤23) vs. MMSE (≤26), sensitivity: 0.78 vs. 0.71; specificity: 0.80 vs. 0.82; positive predictive value: 0.84 vs. 0.84; negative predictive value: 0.72 vs. 0.67; and correctly classified 78.6% vs. 75.5%; p = 0.42). The AUCs of MMSE and MoCA were improved significantly by the SDMT (AUCs: MMSE+SDMT 0.90 (95% CI 0.87-0.93), p <0.001; MoCA+SDMT 0.91 (95% CI 0.88-0.94), p < 0.02). CONCLUSIONS: The MoCA and MMSE are equivalent and moderately sensitive, and can be supplemented with the SDMT to improve their accuracy in VCI screening.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".