A Canadian perspective on severe acute respiratory syndrome coronavirus 2 infection and treatment: how prevalent underlying inflammatory disease contributes to pathogenesis
Bibliographic record
Abstract
The coronavirus disease 2019 (COVID-19), a serious respiratory illness caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has emerged as a global pandemic. Canada reported its first case of COVID-19 on the 25th January 2020. By March 2020, the virus had spread within Canadian communities reaching the most frail and vulnerable elderly population in long-term care facilities. The majority of cases were reported in the provinces of Quebec, Ontario, Alberta, and British Columbia, and the highest mortality was seen among individuals aged 65 years or older. Canada has the highest prevalence and incidence rates of several chronic inflammatory diseases, such as multiple sclerosis, inflammatory bowel disease, and Parkinson's disease. Many elderly Canadians also live with comorbid medical illnesses, such as hypertension, diabetes, cardiovascular disease, and chronic lung disease, and are more likely to suffer from severe COVID-19 with a poor prognosis. It is becoming increasingly evident that underlying inflammatory disease contributes to the pathogenesis of SARS-CoV-2. Here, we review the mechanisms behind SARS-CoV-2 infection, and the host inflammatory responses that lead to resolution or progression to severe COVID-19 disease. Furthermore, we discuss the landscape of COVID-19 therapeutics that are currently in development 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".