The Montreal Cognitive Assessment detects cognitive deficits that go unnoticed during clinical observation in the acute phase after stroke
Bibliographic record
Abstract
BACKGROUND: Detecting cognitive impairments early after stroke is essential for appropriate referrals. Although recommended in stroke guidelines, early cognitive screening is not always implemented. We assessed whether the Montreal Cognitive Assessment (MoCA) adds diagnostic value compared to clinical observation alone. In addition, discharge destinations for stroke patients with and without cognitive deficits detected with the screening tool or the treatment team were explored. METHODS: Forty-four stroke patients were screened with the MoCA during stroke unit admission. Their charts were studied for cognitive impairments reported by the stroke care team, who were blinded to screening scores. Proportions of detected cognitive deficits were compared between screening (score <26) and patient charts. Discharge destination distribution (home vs. rehabilitation) was explored. RESULTS: < 0.001). The distribution of discharge destination did not suggest an association with the detection of cognitive deficits by the treatment team or the cognitive screening. CONCLUSIONS: The MoCA detects more cognitive deficits than clinical impression alone, emphasizing the importance of standard screening for cognitive impairments in acute stroke patients. Ultimately, systematic screening may enhance discharge planning and improve long-term outcomes.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".