Systematic review and meta-analysis of tocilizumab in persons with coronavirus disease-2019 (COVID-19)
Bibliographic record
Abstract
Abstract We performed a meta-analysis to determine safety and efficacy of tocilizumab in persons with coronavirus disease-2019 (COVID-19). We searched PubMed, Web of Science and Medline using Boolean operators for studies with the terms coronavirus OR COVID-19 OR 2019-nCoV OR SARS-CoV-2 AND tocilizumab. Review Manager 5.4 was used to analyze data and the modified Newcastle–Ottawa and Jadad scales for quality assessment. We identified 32 studies in 11,487 subjects including three randomized trials and 29 cohort studies with a comparator cohort, including historical controls (N = 5), a matched cohort (N = 12), or concurrent controls (N = 12). Overall, tocilizumab decreased risk of death (Relative Risk [RR] = 0.74; 95% confidence interval [CI], 0.59, 0.93; P = 0.008; I2 = 80%) but not of surrogate endpoints including ICU admission (RR = 1.40 [0.64,3.06]; P = 0.4; I2 = 88%), invasive mechanical ventilation (RR = 0.83 [0.57,1.22]; P = 0.34; I2 = 65%) or secondary infections (RR = 1.30 [0.97,1.74]; P = 0.08; I2 = 65%) and increased interval of hospitalization of subjects discharged alive(mean difference [MD] = 2 days [<1, 4 days]; P = 0.006; I2 = 0). RRs of death in studies with historical controls (RR = 0.28 [0.16,0.49; P < 0.001]; I2 = 62%) or a matched cohort (RR = 0.68 [0.53, 0.87]; P = 0.002; I2 = 42%) were decreased. In contrast, RRs of death in studies with a concurrent control (RR = 1.10 [0.77, 1.56]; P = 0.60; I2 = 85%) or randomized (RR = 1.18 [0.57,2.44]; P = 0.66; I2 = 0) were not decreased. A reduced risk of death was not confirmed in our analyses which questions safety and efficacy of tocilizumab in persons with COVID-19.
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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 | 0.018 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.043 |
| Bibliometrics | 0.006 | 0.006 |
| 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) | 0.004 | 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".