Diagnostic Value of the Mini-Cog Test in Patients with Chronic Heart Failure 65 Years and Older
Bibliographic record
Abstract
Currently, the number of patients with heart failure (HF) and cognitive impairment (CI) is growing. In this regard, it is necessary to screen for CI in patients with HF. The Mini-Сog test is one of the screening tests, but more research is needed to examine the feasibility of using it on a cohort of cardiac patients. Aim of the study. The aim of the study is to assess the sensitivity and specificity of the Mini-Сog test in identifying patients with CI among patients aged 65 and over with HF. Materials and methods. From March 2021 to March 2023, 149 people aged 65 and older with chronic heart failure (CHF) were selected from a separate structural unit of the Russian Gerontology Research and Clinical Center of the Pirogov Russian National Research Medical University. Cognitive status was assessed using the Montreal Cognitive Assessment Scale and the Mini-Mental State Examination. All patients underwent the Mini-cog testing. Statistical analysis was performed using ROC analysis. Results and conclusions. The prevalence of cognitive impairment among patients with CHF aged 65 or older, according to our study, is 67.8%. A cutoff value of 2 points or less points on the Mini-Cog test (AUC 0.856; CI 95% 0.7750.936, p < 0.001) indicates the presence of severe cognitive impairment with a sensitivity of 61.5% and a specificity of 92.1%. A score of 3 points or lower (AUC 0.828; CI 95% 0.762-0.894, p < 0.001) indicates mild cognitive impairment (MCI) with a sensitivity of 55.4% and a specificity of 93.7%, and dementia with a sensitivity of 80.8% and a specificity of 69.1%.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".