Comparison of Montreal Cognitive Assessment and Mini- Mental State Examination in Evaluating Cognitive Domain Deficit Following Aneurysmal Subarachnoid
Bibliographic record
Abstract
Objective: Cognitive deficits are common after aneurysmal subarachnoid haemorrhage (aSAH), and clinical evaluation is important for their management. Our hypothesis was that the Montreal Cognitive Assessment (MoCa) is superior to the Mini-Mental State Examination (MMSE) in screening for cognitive domain deficit in aSAH patients. Methods: We carried out a prospective observational and diagnostic accuracy study on Hong Kong aSAH patients aged 21 to 75 years who had been admitted within 96 hours of ictus. The domain-specific neuropsychological assessment battery, the MoCA and MMSE were administered 2–4 weeks and 1 year after ictus. A cognitive domain deficit was defined as a cognitive domain z score,21.65 (below the fifth percentile). Cognitive impairment was defined as two or more cognitive domain deficits. The study is registered at ClinicalTrials.gov of the US National Institutes of Health (NCT01038193). Results: Both the MoCA and the MMSE were successful in differentiating between patients with and without cognitive domain deficits and cognitive impairment at both assessment periods. At 1 year post-ictus, the MoCA produced higher area under the curve scores for cognitive impairment than the MMSE (MoCA, 0.92; 95 % CI, 0.83 to 0.97 versus MMSE, 0.77; 95% CI, 0.66 to 0.83, p = 0.009). Interpretation: Cognitive domain deficits and cognitive impairment in patients with aSAH can be screened with the MoCA
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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".