Diretrizes Brasileiras para o tratamento farmacológico de pacientes hospitalizados com COVID-19
Bibliographic record
Abstract
Introduction: Several therapies are being used or proposed for COVID-19, and many lack appropriate evaluations of their effectiveness and safety. The purpose of this document is to develop recommendations to support decisions regarding the pharmacological treatment of patients hospitalized with COVID-19 in Brazil. Methods: A group of 27 experts, including representatives of the Ministry of Health and methodologists, created this guideline. The method used for the rapid development of guidelines was based on the adoption and/or adaptation of existing international guidelines (GRADE ADOLOPMENT) and supported by the e-COVID-19 RecMap platform. The quality of the evidence and the preparation of the recommendations followed the GRADE method. Results: Sixteen recommendations were generated. They include strong recommendations for the use of corticosteroids in patients using supplemental oxygen, the use of anticoagulants at prophylactic doses to prevent thromboembolism and the nonuse of antibiotics in patients without suspected bacterial infection. It was not possible to make a recommendation regarding the use of tocilizumab in patients hospitalized with COVID-19 using oxygen due to uncertainties regarding the availability of and access to the drug. Strong recommendations against the use of hydroxychloroquine, convalescent plasma, colchicine, lopinavir + ritonavir and antibiotics in patients without suspected bacterial infection and also conditional recommendations against the use of casirivimab + imdevimab, ivermectin and rendesivir were made. Conclusion: To date, few therapies have proven effective in the treatment of hospitalized patients with COVID-19, and only corticosteroids and prophylaxis for thromboembolism are recommended. Several drugs were considered ineffective and should not be used to provide the best treatment according to the principles of evidence-based medicine and promote economical resource use.
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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 | 0.012 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".