Brazilian guidelines for the treatment of outpatients with suspected or confirmed COVID-19. A joint guideline of the Brazilian Association of Emergency Medicine (ABRAMEDE), Brazilian Medical Association (AMB), Brazilian Society of Angiology and Vascular Surgery (SBACV), Brazilian Society of Geriatrics and Gerontology (SBGG), Brazilian Society of Infectious Diseases (SBI), Brazilian Society of Family and Community Medicine (SBFMC), and Brazilian Thoracic Society (SBPT)
Bibliographic record
Abstract
BACKGROUND: Several therapies have been used or proposed for the treatment of COVID-19, although their effectiveness and safety have not been properly evaluated. The purpose of this document is to provide recommendations to support decisions about the drug treatment of outpatients with COVID-19 in Brazil. METHODS: A panel consisting of experts from different clinical fields, representatives of the Brazilian Ministry of Health, and methodologists (37 members in total) was responsible for preparing these guidelines. A rapid guideline development method was used, based on the adoption and/or adaptation of recommendations from existing international guidelines combined with additional structured searches for primary studies and new recommendations whenever necessary (GRADE-ADOLOPMENT). The rating of quality of evidence and the drafting of recommendations followed the GRADE method. RESULTS: Ten technologies were evaluated, and 10 recommendations were prepared. Recommendations were made against the use of anticoagulants, azithromycin, budesonide, colchicine, corticosteroids, hydroxychloroquine/chloroquine alone or combined with azithromycin, ivermectin, nitazoxanide, and convalescent plasma. It was not possible to make a recommendation regarding the use of monoclonal antibodies in outpatients, as their benefit is uncertain and their cost is high, with limitations of availability and implementation. CONCLUSION: To date, few therapies have demonstrated effectiveness in the treatment of outpatients with COVID-19. Recommendations are restricted to what should not be used, in order to provide the best treatment according to the principles of evidence-based medicine and to promote resource savings by aboiding ineffective treatments.
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.
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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".