Mini-Mental State Examination not optimal for cognitive deficits in elderly heart failure patients
Bibliographic record
Abstract
1 that compared the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) test for the identification of cognitive deficits (CDs) in elderly heart failure (HF) patients.The study employed a cross-sectional design in which the MMSE and MoCA CD test were administered to 43 elderly HF patients with a mean age of 67 years by using a clinical questionnaire on sociology.The data collected were analyzed using R Studio software, and Fisher's exact test was used to determine the differences between the MMSE and MoCA test, although the authors arrived at the conclusion that the MMSE performed better than the MoCA test in the identification of CDs in elderly patients with HF through their analysis.However, we believe that this study was not rigorous.Therefore, the authors must resolve the following issues to improve rigor.First, there is the issue of the participants' level of education.Although the authors clearly describe in their article that the test results were adjusted for the individual's level of education, such as limitations on scores for different years of education and the corresponding bonus points, this was fatal to the conclusion.The references used by the authors also mention that the MoCA test is not only inaccurate in detecting cognitive impairment no dementia (CIND) in people with low levels of education 2 but also has a higher sensitivity to cognitive decline in patients with higher levels of education 3 .This suggests that the MoCA test can vary depending on the level of education.In contrast, the present study analyzed groups with different levels of education together, and although the results of the study showed a statistically significant difference between the MMSE and MoCA test (p=0.045),there was also a risk of false-positive results.Therefore, the authors need to differentiate the educational level of the participating population and conduct the study separately.Second, the issue of reliability and internal consistency of MMSE and MoCA test.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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; both teacher heads agree on what is shown here.
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".