Tocilizumab in addition to standard of care in the management of COVID-19: a meta-analysis of RCTs.
Bibliographic record
Abstract
OBJECTIVE: We performed a systematic review and meta-analysis for exploring clinical benefits and safety of tocilizumab in addition to standard of care (SOC) in treating patients with coronavirus disease 2019 (COVID-19). METHODS: An electronic search was carried out in PubMed, EMBASE, Cochrane Library, and Science Direct, as well as in medRxiv preprint server, to identify eligible studies. Only randomized Controlled Trials (RCTs) that compared mortality events and/or adverse events between a tocilizumab + SOC group and a SOC-only control group were included. The primary outcome was 28-day mortality. Secondary outcomes include progression to severe disease, defined as need for mechanical ventilation (MV) or intensive care unit (ICU) admission, and adverse events (AE). RESULTS: A total of nine studies (6,490 participants) could be included in this meta-analysis, with 3,358 participants in the tocilizumab + SOC group and 3,132 participants in the SOC-only group. The overall mortality rate was lower in the tocilizumab group compared to the SOC-only group, though the difference was not statistically significant (odds ratio [OR], 0.87; 95% CI, 0.73-1.04; I2, 15%). This finding was unaffected by subgroup analyses based on initial use of steroids or mechanical ventilation at baseline. Patients receiving tocilizumab were 26% less likely to progress to MV, and this difference was statistically significant (OR, 0.74; 95% CI, 0.64-0.86; I2, 0%). Among patients who were not in ICU at randomization, the tocilizumab group had 34 % lower rate of ICU admission compared to the SOC-only group (OR, 0.66; 95% CI, 0.40-2.14; I2, 29%). The occurrence of serious infections was lower in the tocilizumab group (OR, 0.57; 95% CI, 0.36-0.89; I2, 21%). CONCLUSION: Tocilizumab is generally well-tolerated in COVID-19. Although this drug does not appear to have a significant benefits on survival, it may have a role in preventing progression to intensive care and MV.
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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 | 0.022 | 0.036 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.062 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".