Screening for mild cognitive impairment in patients with heart failure: Montreal Cognitive Assessment versus Mini Mental State Exam
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Cognitive impairments occur frequently in patients with chronic heart failure (CHF), resulting in worse health outcomes than expected. These impairments can remain undetected unless specifically screened. There are limited sensitive screening measures available in nursing practice to identify mild cognitive impairment (MCI). AIM: To compare the Montreal Cognitive Assessment (MoCA) with the Mini Mental State Exam (MMSE) in screening for MCI in CHF patients. METHODS: The MMSE and MoCA were administered to 93 hospitalized CHF patients (70±11 years), without a history of neurocognitive problems. Patients with low MoCA scores (<26) were compared to those with low MMSE scores (<27). Two different parameters were examined between the MoCA and the MMSE: level of MCI agreement (Kappa coefficient) and task errors on assessed cognitive domains (χ2 test). RESULTS: Statistically more patients had low MoCA scores compared with low MMSE scores (66 vs. 30, p=0.02). The MoCA classified 38 (41%) patients as cognitively impaired that were not classified by the MMSE. A significantly low level of agreement was found (κ=0.25, p=0.001) between the MMSE and MoCA in identifying patients with scores suggestive of MCI. More task errors were observed on the MoCA cognitive domains compared with the MMSE cognitive domains. In 68% of patients with low cognitive scores, visuospatial task errors were observed on tasks from the MoCA compared with 22% on a similar task of the MMSE. CONCLUSION: The MoCA, a screening tool for MCI, identified subtle but potentially clinically relevant cognitive dysfunctions with greater frequency than MMSE.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 it