Antirheumatic drugs for COVID-19 treatment based on the phases of the disease: Current concept
Bibliographic record
Abstract
COVID-19 disease is the most recent pandemic, since it has affected more than four and a half million people and caused more than 300,000 deaths. It is a very complex systemic disease in terms of pathogenesis, treatment, and prognosis. Pharmacological treatment may include antiviral and antimalarial drugs, antibiotics, monoclonal antibodies, corticosteroids as well as low-molecular-weight heparins to prevent the evolution of the disease from reaching the severe inflammatory phase that can lead to respiratory complications, multiple organ failure, disseminated intravascular coagulation (DIC), and finally death. Therefore, pending the development of the much sought-after vaccine, there needs to be a multidisciplinary approach to tackling this disease, and it is essential to use different medical treatments at the correct pathogenic moment. The aim of this article is to evaluate the rationale and reason behind the use of antirheumatic drugs, by expert point of view, in the various phases of the disease. Another important aspect in the management of the disease is to identify patients at high risk, both to change their lifestyle and to correct the state of their health through non-pharmacological measures for improving their immuno-balance. Our literature review reveals the important role and the therapeutic potential of antirheumatic agents in preventing the progression of the disease and aiding recovery from the disease. However, there is a lack of clinical evidence to support the use of these agents, indicating that further randomized controlled studies are required.
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How this classification was reachedexpand
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".