COVID-19 pathophysiology and pharmacology: what do we know and how did Canadians respond? A review of Health Canada authorized clinical vaccine and drug trials
Bibliographic record
Abstract
Coronavirus disease 2019 (COVID-19) has resulted in the death of over 18 000 Canadians and has impacted the lives of all Canadians. Many Canadian research groups have expanded their research programs to include COVID-19. Over the past year, our knowledge of this novel disease has grown and has led to the initiation of a number of clinical vaccine and drug trials for the prevention and treatment of COVID-19. Here, we review SARS-CoV-2 (the coronavirus that causes COVID-19) and the natural history of COVID-19, including a timeline of disease progression after SARS-CoV-2 exposure. We also review the pathophysiological effects of COVID-19 on the organ systems that have been implicated in the disease, including the lungs, upper respiratory tract, immune system, central nervous system, cardiovascular system, gastrointestinal organs, the liver, and the kidneys. Then we review general therapeutics strategies that are being applied and investigated for the prevention or treatment of COVID-19, including vaccines, antivirals, immune system enhancers, pulmonary supportive agents, immunosuppressants and (or) anti-inflammatories, and cardiovascular system regulators. Finally, we provide an overview of all current Health Canada authorized clinical drug and vaccine trials for the prevention or treatment of COVID-19.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".