Mapping cognitive function screening instruments for patients with heart failure: A scoping review
Bibliographic record
Abstract
Background: Patients with heart failure (HF) often experience cognitive impairment, which negatively affects their quality of life. An effective screening tool is essential for nurses and healthcare professionals to assess cognitive function as part of HF management. Although many instruments exist, none are specifically designed for patients with HF. Objective: This study aimed to map the instruments for screening cognitive function in patients with HF. Design: A scoping review. Data Sources: Articles published between 2019 and 2023 were searched in PubMed, ScienceDirect, and Google Scholar, with the last search conducted on 27 January 2024. Review Methods: The review followed the scoping review framework by Arksey and O'Malley and adhered to PRISMA guidelines for scoping reviews. Results: Of the 21 articles meeting inclusion criteria, six cognitive function screening instruments were used across various cognitive domains, effectively identifying cognitive impairment in both inpatient and outpatient HF settings. The Montreal Cognitive Assessment (MoCA) was the most frequently used tool, covering a broad range of cognitive domains. MoCA showed high efficacy with a kappa coefficient of 0.82, Cronbach's alpha reliability of 0.75, sensitivity of 90%, and specificity of 87%. Conclusion: Instruments like MoCA, Mini-Cog, and TICS-m show promise for assessing cognitive function in patients with HF, each with specific strengths and limitations. MoCA is notable for its comprehensive coverage despite being time-consuming and having language barriers. Further research is needed to revalidate and improve the existing instruments. It is crucial for nurses and healthcare professionals to integrate these tools into regular patient management, highlighting the need for continued research in their application.
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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 | 0.029 | 0.153 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.033 | 0.026 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".