Diagnostic accuracy of cognitive screening tools under different neuropsychological definitions for poststroke cognitive impairment
Bibliographic record
Abstract
OBJECTIVES: The accuracy of cognitive screening tools to detect poststroke cognitive impairment (PSCI) was investigated using various neuropsychological definitions. METHODS: Hospital-based stroke patients underwent a comprehensive neuropsychological assessment. The rate of PSCI was estimated using thresholds of 1, 1.5, or 2 standard deviations below the normal control and memory impairment defined by a single or multiple tests. Meanwhile, the diagnostic accuracy of cognitive screening through face-to-face assessment using the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment Scale (MoCA), and telephone assessment using a 5-minute NINDS-Canadian Stroke Network (NINDS-CSN) scale and a six-item screener (SIS), was both tested under different definitions, with the optimal cutoff selected based on the highest Youden index. RESULTS: In stroke patients, the rate of PSCI ranged from 46.3% to 76.3% upon different definitions. The face-to-face MoCA was more consistent with the comprehensive cognitive assessment compared to MMSE. The optimal cutoff of PSCI was MMSE ≤ 27 and MoCA ≤ 19. For the telephone tests, the 5-minute NINDS-CSN assessment was more reliable, and the optimal cutoff was ≤23, while for SIS ≤ 4. CONCLUSIONS: Cognitive screening tools including the face-to-face MMSE and MoCA, together with the telephone assessment of NINDS-CSN 5-minute protocol and SIS, were simple and effective for detecting PSCI in stroke patients. The corresponding threshold values for PSCI were 27 points, 19 points, 23 points, and 4 points.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".