Effects of immunomodulatory therapies on COVID-19 prognosis in moderate-to-critically ill patients: A systematic review
Bibliographic record
Abstract
Background: Hyper-inflammatory response to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) resulting from excess immunological activities, usually called the cytokine storm, has been associated with severe illness and poor prognosis of COVID-19. This systematic review aimed to evaluate available evidence for associated effects of immune-modulators in Coronavirus disease 2019 (COVID-19) therapy for informed clinical decisions. Methods: A systematic review was conducted with search for eligible articles in the databases of Cochrane Library, Embase, PubMed, Scopus and MedRxiv.org up to 25 August 2020. Using relevant keywords for the search, studies on the use of immunotherapy in COVID-19 were considered eligible, but only original articles were included. Case reports, reviews, commentaries, and correspondences were excluded. Risks of bias of individual studies was assessed by the Newcastle- Ottawa scale for observational studies. Results: A total of 771 articles were screened and 24 clinical studies were included. Among these were 3 studies on anakinra, 1 study each on itolizumab and siltuximab, and 19 studies on tocilizumab in the therapy of moderate-to-critical COVID-19. Findings showed that all the clinical studies but 3, demonstrated good clinical outcomes associated with immune-modulatory therapies in COVID-19, but these studies had several limitations at the study and outcome levels. Conclusion: The reviewed studies demonstrated the potential efficacy of immunomodulators to improve clinical outcomes in COVID-19 patients, including older patients with several comorbidities. This supports the necessity of randomized trials of these drugs in large populations.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".