Effect of various treatment modalities on the novel coronavirus (nCOV-2019) infection in humans: a systematic review & meta-analysis
Bibliographic record
Abstract
Abstract Background and aim Several therapeutic agents have been investigated for the treatment of novel Coronavirus-2019 (nCOV-2019). We aimed to conduct a systematic review and meta-analysis to assess the effect of various treatment modalities in nCOV-2019 patients. Methods An extensive literature search was conducted before 22 May 2020 in PubMed, Google Scholar, Cochrane library databases. Quality assessment was performed using Newcastle Ottawa Scale. A fixed-effect model was applied if I 2 <50%, else the results were combined using random-effect model. Risk Ratio (RR) or Standardized Mean Difference (SMD) along-with 95% Confidence Interval (95%CI) were used to pool the results. Between study heterogeneity was explored using influence and sensitivity analyses & publication bias was assessed using funnel plots. Entire statistical analysis was conducted in R version 3.6.2. Results Eighty-one studies involving 44 in vitro and 37 clinical studies including 8662 nCOV-2019 patients were included in the review. Lopinavir-Ritonavir compared to controls was significantly associated with shorter mean time to clinical improvement (SMD -0.32; 95%CI -0.57 to -0.06) and Remdesivir compared to placebo was significantly associated with better overall clinical improvement (RR 1.17; 95%CI 1.07 to 1.29). Hydroxychloroquine was associated with less overall clinical improvement (RR 0.88; 95%CI 0.79 to 0.98) and longer time to clinical improvement (SMD 0.64; 95%CI 0.33 to 0.94), It additionally had higher all-cause mortality (RR 1.6; 95%CI 1.26 to 2.03) and more total adverse events (RR 1.84; 95% CI 1.58 to 2.13). Conclusion Our meta-analysis suggests that except in vitro studies, no treatment till now has shown clear-cut benefit on nCOV-2019 patients. Lopinavir-Ritonavir and Remdesivir have shown some benefits in terms less time to clinical improvement and better overall clinical improvement. Hydroxychloroquine use has a risk of higher mortality and adverse events. Results from upcoming large clinical trials must be awaited to draw any profound conclusions.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.038 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".