COVID-19 Pandemic: Global Impact and Potential Implications for Cardiovascular Disease in Canada
Bibliographic record
Abstract
BACKGROUND: The literature indicates that cardiovascular disease (CVD; including stroke), older age, and availability of health care resources affect COVID-19 case fatality rates (CFRs). The cumulative effect of COVID-19 CFRs in global CVD populations and the extrapolated effect on access to health care services in the CVD population in Canada are not fully known. In this study we explored the relationships of factors that might affect COVID-19 CFRs and estimated the potential indirect effects of COVID-19 on Canadian health care resources. METHODS: Country-level epidemiological data were analyzed to study the correlation, main effect, and interaction between COVID-19 CFRs and: (1) the proportion of the population with CVD; (2) the proportion of the population 65 years of age or older; and (3) the availability of essential health services as defined by the World Health Organization Universal Health Coverage index. For indirect implications on health care resources, estimates of the volume of postponed coronary artery bypass grafting, percutaneous coronary intervention, and valve surgeries in Ontario were calculated. RESULTS: = 0.03). For every 1% increase in the proportion of the population 65 years of age or older or proportion of the population with CVD, the COVID-19 CFR was 9% and 19% higher, respectively. Approximately 1252 procedures would be postponed monthly in Ontario because of current public health measures. CONCLUSIONS: Countries with more prevalent CVD reported higher COVID-19 CFRs. Strain on health care resources is likely in Canada.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".