CONVALESCENT plasma for COVID‐19: A meta‐analysis of clinical trials and real‐world evidence
Bibliographic record
Abstract
Abstract Background There is still a lack of consensus on the efficacy of convalescent plasma (CP) treatment in COVID‐19 patients. We performed a systematic review and meta‐analysis to investigate the efficacy of CP vs standard treatment/non‐CP on clinical outcomes in COVID‐19 patients. Methods Cochrane Library, PubMed, EMBASE and ClinicalTrials.gov were searched from December 2019 to 16 July 2021, for data from clinical trials and observational studies. The primary outcome was all‐cause mortality. Risk estimates were pooled using a random‐effect model. Risk of bias was assessed by Cochrane Risk of Bias tool for clinical trials and Newcastle‐Ottawa Scale for observational studies. Results In total, 18 peer‐reviewed clinical trials, 3 preprints and 26 observational studies met the inclusion criteria. In the meta‐analysis of 18 peer‐reviewed trials, CP use had a 31% reduced risk of all‐cause mortality compared with standard treatment use (pooled risk ratio [RR] = 0.69, 95% confidence interval [CI]: 0.56‐0.86, P = .001, I2 = 50.1%). Based on severity and region, CP treatment significantly reduced risk of all‐cause mortality in patients with severe and critical disease and studies conducted in Asia, pooled RR = 0.61, 95% CI: 0.47‐0.81, P = .001, I2 = 0.0%; pooled RR = 0.67, 95% CI: 0.49‐0.92, P = .013, I2 = 0.0%; and pooled RR = 0.62, 95% CI: 0.48‐0.80, P < .001, I2 = 20.3%, respectively. The meta‐analysis of observational studies showed the similar results to the clinical trials. Conclusions Convalescent plasma use was associated with reduced risk of all‐cause mortality in severe or critical COVID‐19 patients. However, the findings were limited with a moderate degree of heterogeneity. Further studies with well‐designed and larger sample size are needed.
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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 | 0.028 | 0.056 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.052 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".