Efficacy and safety of tocilizumab in the management of COVID-19: A systematic review and meta-analysis of observational studies
Bibliographic record
Abstract
OBJECTIVES: This systematic review and meta-analysis was aimed to evaluate the efficacy and safety of tocilizumab (TCZ) in treating severe coronavirus disease 2019 (COVID-19). METHODS: The electronic search was made using PubMed, Scopus, CENTRAL, and Google scholar to identify the retrospective observational reports. The studies published from 01 January 2020 to 30th October 2020. Participants were hospitalised COVID-19 patients. Interventions included tocilizumab versus placebo/standard of care. The comparison will be between TCZ versus standard of care (SOC)/placebo. Inconsistency between the studies was evaluated with I2 and quality of the evidences were evaluated by Newcastle-Ottawa scale. RESULTS: Based on the inclusion criteria there were 24 retrospective studies involving 5686 subjects were included. The outcomes of the meta-analysis have revealed that the TCZ has reduced mortality (M-H, RE-OR -0.11(-0.18--0.04) 95% CI, p=0.001, I2 =88%) and increased the incidences of super-infections (M-H, RE-OR 1.49(1.13-1.96) 95% CI, p=0.004, I2=47%). However, there is no significant difference in ICU admissions rate (M-H, RE-OR -0.06(-0.23-0.12), I2=93%), need for mechanical ventilation (M-H, RE-OR of 0.00(-0.06-0.07), I=74%), LOS (IV -2.86(-0.91-3.38), I2=100%), LOS-ICU (IV: -3.93(-12.35-4.48), I2=100%), and incidences of pulmonary thrombosis (MH, RE-OR 1.01 (0.45-2.26), I2=0%) compared to SOC/control. CONCLUSIONS: Based on cumulative low-to-moderate certainty evidence shows that TCZ could reduce the risk of mortality in hospitalised patients. However, there is no statistically significant difference observed between the TCZ and SOC/control groups in other parameters.
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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 | 0.021 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.045 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".