A Systematic Review of Coronavirus Disease 2019 with Respiratory Distress Syndrome in Adult: Focus on Risk Factors, Mechanism, Diagnosis, and Treatment
Bibliographic record
Abstract
Context: Coronavirus disease 2019 (COVID-19) is a newly discovered disease, caused by SARS-CoV-2 and currently has become a pandemic. Acute respiratory distress syndrome (ARDS) is the most common complication in COVID-19. There were numerous conflicting results among articles related to it. The aim of this study is to ascertain the most compelling evidence about ARDS in COVID-19. Evidence Acquisition: This systematic review was registered in PROSPERO (CRD42020180379). A systematic search was conducted in PubMed, PubMed central, and Google Scholar on April 16, 2020. Two reviewers independently searched and selected the articles. The risk of bias was evaluated using the Newcastle-Ottawa Quality assessment tool. Results: A total of 1,647 articles were screened, 9 articles were included. Patients were classified as having various degrees of ARDS, the diagnosis of COVID-19 was confirmed by PCR nasopharyngeal swab. Risk factors of ARDS in COVID-19 reported were older age, male gender, and pre-existing medical conditions. Cytokine storm was thought to play a role in the mechanism of ARDS. The main treatment for COVID-19 was supportive and symptomatic. To date, there is no antiviral treatment recommended for COVID-19 and the given treatment for ARDS in COVID-19 was similar to other pneumonia-induced ARDS. No additional therapy specific for ARDS in COVID-19 has been proposed. Conclusion: Our synthesis of the literature showed that there was no good evidence in the mechanism and treatment of ARDS. Further translation research in the mechanism of ARDS and continuing with clinical trials evaluating drug efficacy for ARDS in COVID-19 is needed.
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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 | 0.010 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".