Improving diagnostic accuracy of the Montreal Cognitive Assessment to identify post-stroke cognitive impairment
Bibliographic record
Abstract
Given advantages in reperfusion therapy leading to mild stroke, less apparent cognitive deficits can be overseen in a routine neurological examination. Despite the widespread use of the Montreal Cognitive Assessment (MoCA), age- and education-specific cutoffs for the detection of post-stroke cognitive impairment (PSCI) are not established, hampering its valid application in stroke. We aimed to establish age- and education-specific MoCA cutoffs to better discriminate patients with and without acute PSCI. Patients with acute ischemic stroke underwent the MoCA and a detailed neuropsychological assessment. PSCI was defined as a performance < − 1.5 SD in ≥ 2 cognitive domains. As secondary data analysis, the discriminant abilities of the MoCA raw -score (not adding + 1 as correction for ≤ 12 years of education, YoE) cutoffs were automatically derived based on Youden Index and evaluated by receiver operating characteristic analyses across age- (< 55, 55–70, > 70 years old) and education-specific (≤ 12 and > 12 YoE) groups. 351 stroke patients (67.4 ± 14.1 years old; 13.1 ± 2.8 YoE) underwent the neuropsychological assessment 2.7 ± 2.0 days post-stroke. The original MoCA cutoff < 26 falsely classified 26.2% of examined patients, with poor sensitivity in younger adults (34.8% in patients < 55 years > 12 YoE) and poor specificity in older adults (55.0%, in > 70 years ≤ 12 YoE). By maximizing both sensitivity and specificity, the optimal MoCA raw cutoffs were: (i) < 28 in patients aged < 55 with > 12 YoE (sensitivity = 69.6%, specificity = 77.8%); (ii) < 22 and < 25 in patients > 70 years with ≤ 12 and > 12 YoE (sensitivity = 61.6%, specificity = 90.0%; sensitivity = 63.3%, specificity = 84.0%, respectively). In other groups the optimal MoCA raw cutoff was < 26. Age and education level should be considered when interpreting MoCA-scores. Though new age- and education-specific cutoffs demonstrated higher discriminant ability for PSCI, their performance in young stroke and adults with higher education level was low due to ceiling effects and MoCA subtests structure, and cautious interpretation in these patients is warranted. Trial registration : ClinicalTrials.gov Identifier: NCT05653141.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".