Eficacia de las intervenciones farmacológicas para el tratamiento de la COVID-19
Bibliographic record
Abstract
Introduction. Despite the high morbidity of coronavirus disease, there is still no effective treatment to address it. Objective. Establish the effectiveness of pharmacological interventions in the treatment of adults diagnosed with coronavirus disease at any stage. Methods. An exploratory review examined publications up to January 21, 2021 identified by a search on MEDLINE, Cochrane, medRxiv, New England Journal via PubMed. Viral activity reducing drugs, corticosteroids, immunerelated therapy were analyzed to evaluate the outcomes of survival, mechanical ventilation, hospital stay and safety after application of the drugs in patients in mild, moderate and/or severe phase of the disease. Controlled and randomized clinical trials were prioritized, the risk of which was determined by the Newcastle-Ottawa tools and A measurement Tool to Assess Systematic Reviews 2. Results. It was found that interferón-α2b decreases the duration of virus elimination and inflammatory markers. In autoimmune therapy, tocilizumab showed little efficacy when administered uniquely, however when combined with dexamethasone enhances its effect. Conclusion. One year after the coronavirus-19 pandemic, there is no conclusive evidence of its therapy. To date, some efficacy of inhaled Interferon-α2b, as well as tocilizumab and dexamethasone in single or combined administration, have been proven.
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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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".