Psychic and Cognitive Impacts of Cardiovascular Disease: Evidence from an Observational Study and Comparison by a Systematic Literature Review
Bibliographic record
Abstract
Background/Objectives: Cardiovascular diseases (CVDs) are frequently associated with psychiatric and cognitive comorbidities. These conditions have been shown to significantly impact quality of life and clinical outcomes. This study aims to evaluate the prevalence of anxiety, depression, and cognitive deficits in patients with CVD and to compare the results with existing evidence in the literature. Methods: A total of 74 patients were assessed using the following standardized screening tools: Montreal Cognitive Assessment (MoCA), Mini-Mental State Examination (MMSE), Beck Depression Inventory-II (BDI-II), and Beck Anxiety Inventory (BAI). A systematic review was then conducted to compare the findings with those reported in the literature. Results: Most previous studies using the MoCA reported an over 70% absence of cognitive impairment, whereas this study shows a balanced distribution between the absence of (32.4%) and mild (35%) or moderate (32%) impairment. Studies with the MMSE indicated high rates of absence of cognitive deficits (74–79%), but here, the rate of absence was lower (58%), with an increase in mild impairment (42%). Regarding depression, compared with studies showing only absence or moderate/severe forms, this study reveals a more balanced profile, with 57% without depression and with varying severity levels (22% mild, 19% moderate, and 3% severe). Finally, for anxiety, unlike previous asymmetric distributions, greater variability was observed, with 58% without anxiety and significant percentages of mild (26%), moderate (12%), and severe (4%) anxiety. Conclusions: The results highlight a significant and varied prevalence of anxiety, depression, and cognitive deficits, emphasizing the importance of a multidimensional assessment to improve clinical management and therapeutic outcomes.
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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 | 0.010 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.010 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".