COVID-19 Pandemic: Global Impact and Potential Implications for Cardiovascular Disease in Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it