Relieving the Burden of Cardiovascular Disease in Canada Using Current Therapies: A Literature Review
Bibliographic record
Abstract
Introduction: Cardiovascular disease (CVD) is a leading cause of mortality and morbidity in Canada. In 2016 alone, 91,524 Canadians died of cardiac conditions, strokes, or other vascular cognitive impairments. As a major contributor to reduced quality of life, CVDs are characterized by their effect on the heart or blood vessels and consist of many diseases, including ischemic heart disease, stroke, and heart failure. The established Canadian burden of CVD demands a need for more effective management and reduction of CVD prevalence. This review investigates the current, evidence-based therapies used to combat CVD in patients across Canada. Methods: A literature search was conducted in PubMed using MeSH terms and keywords such as “cardiovascular disease”, “therapy”, “Canada”, and “adult”. Clinical trials and independent studies in English that were published in the last two decades and investigated current therapies for alleviating symptoms or slowing CVD progression were selected and reviewed. Results: Various therapies are used to prevent, manage, or treat CVD, and are often individually reported in literature. However, these therapeutic interventions can be classified into distinct groups such as medication, rehabilitation, lifestyle changes, and surgical devices. Some CVD therapies have been proven to be more effective than others and it is important to apply the findings of these studies to make evidence-based treatment and management decisions. By synthesizing findings about common CVD therapies, a limitation of current literature can be addressed. Discussion: The revision and appraisal of CVD therapies will allow for a more accurate assessment of their clinical applicability, and hence the identification of optimal treatment strategies. Doing so will also improve the feasibility of achieving shared and informed decision-making in a patient-centered care framework. Ultimately, this review can facilitate discussion around cardiovascular health, and provide a comprehensive approach for developing evidence-based health policies targeted towards the global management of CVD. Conclusion: There is a need for evidence-based implementation of therapies to effectively address the burden of CVD. Lack of evidence or knowledge translation will have significant consequences on patient health outcomes. Future research should address other variables such as culture, gender, age, and geography, to expand generalizability.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.019 | 0.028 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".