Feasibility of the <scp>M</scp>ontreal <scp>C</scp>ognitive <scp>A</scp>ssessment in acute stroke patients
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Cognitive deficits are common following stroke. Cognitive function in the acute stroke setting is a predictive factor for mid-term outcome. The Montreal Cognitive Assessment (MoCA) is a screening tool for cognitive impairment. The feasibility of MoCA in the acute phase of stroke was evaluated and factors predictive of cognitive impairment were determined. METHODS: In this prospective, single-centre, explorative and observational study consecutive patients with ischaemic (IS) or haemorrhagic (ICH) stroke were enrolled between March 2011 and September 2012. The routine work-up for each patient encompassed assessment of cardiovascular risk factors, the National Institutes of Health Stroke Scale (NIHSS) and the pre-morbid modified Rankin Scale (mRS) score. Cognitive performance was measured using the German version of the MoCA within the first days of admission. A MoCA score of <26 was considered to indicate cognitive impairment. RESULTS: Between March 2011 and September 2012 a total of 842 patients with IS (89.0%) and ICH (11.0%) were enrolled in our study. MoCA was feasible in 678/842 patients (80.5%). Factors independently associated with non-feasibility were stroke severity (NIHSS), pre-morbid functional status (mRS), age and lower educational level. Mean MoCA was 21.4 (SD 5.7). A total of 498/678 (73.5%) patients appeared cognitively impaired (<26/30). Independent predictive factors for a lower MoCA score were age, educational level, stroke severity (NIHSS) and pre-morbid functional status (mRS). CONCLUSIONS: In the acute phase of stroke, MoCA is feasible in about 80% of eligible patients. At this stage, MoCA identifies a cognitive impairment in 75% of patients.
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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) | 0.000 | 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 | 0.001 | 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".