Test time affects the detection of cognitive dysfunction by Montreal Cognitive Assessment in elderly patients after stroke
Bibliographic record
Abstract
Objective To compare if the Montreal cognitive assessment (MoCA) performed in the morning or afternoon would affect abnormal rate of cognitive function in the elderly with stroke. Methods A total of 378 senile patients (≥ 65 years) with acute ischemic stroke and low NIHSS score (≤ 3) were enrolled in the prospective study, which was held in the Department of Neurology at Cangzhou Hospital of Integrated Traditional Chinese Medicine.MoCA was assessed after one month of hospitalization.Based on the time of MoCA assessment, all patients were randomly divided into the group A (assessed in the morning, 9 am-12 am) and the group B (assessed in the afternoon, 12 am to 5 pm). Clinical data were collected, and RANKIN scale (mRS) examination was performed.Moreover, patients were further divided into severe cognitive impairment (SCI) subgroup (score 26) according to the MoCA score. Results There were 189 patients in the group A (50%), and 189 cases in the group B (50%). There was no significant difference in age, gender, education level, disability (mRS score < 1), history of hypertension, diabetes, hyperlipidemia, smoking and atrial fibrillation between the two groups.Based on the MoCA score, 211 cases had NCI, 142 had MCI, and 25 had SCI.Compared with patients in group B, patients in group A was associated with significantly higher positive rate of SCI[12.2% (23/189)vs.1.1% (2/189), P=0.000], MCI[40.2% (76/189)vs.34.9% (66/189), P=0.013]and slightly higher positive rate of NCI[56.6% (107/189)vs.55.0% (104/189), P=0.214]. Conclusions The test time of MoCA may have an effect on the cognitive function detection rate in elderly patients with stroke, and the time of MoCA examination should be considered in clinical examination. Key words: Cognition Disorders; Stroke; Time
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".