Question · asked by 1 review
Summary of findings table ‐ Convalescent plasma vs human immunoglobulin: Serious adverse events ‐ not reported
- Population
- individuals with moderate to severe disease
- Intervention
- Summary of findings table ‐ Convalescent plasma
- Comparison
- human immunoglobulin
- Outcome
- Serious adverse events ‐ not reported
The review found no eligible studies for this question.
What each review found
Current versions onlyNo studies found 2 · 100%GRADE not reported 0 · 0%Very low 0 · 0%Low 0 · 0%Moderate 0 · 0%High 0 · 0%
| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Summary of findings table ‐ Convalescent plasma vs human immunoglobulin Serious adverse events ‐ not reported · individuals with moderate to severe disease The review found no eligible studies for this question. Convalescent plasma for people with COVID‐19: a living systematic review (2023) · Cochrane Headline outcomes onlyearlier version CD013600.PUB5, superseded | no estimate extracted | Not reported | No studies found |
Summary of findings table ‐ Convalescent plasma vs human immunoglobulin Serious adverse events ‐ not reported · individuals with moderate to severe disease The review found no eligible studies for this question. Convalescent plasma for people with COVID‐19: a living systematic review (2023) · Cochrane Headline outcomes only | no estimate extracted | Not reported | No studies found |
Findings are shown side by side, never pooled across reviews.
The reviews
| Review | Searched up to | Included studies | AMSTAR 2 |
|---|---|---|---|
| Convalescent plasma for people with COVID‐19: a living systematic review 2023 · Cochrane Headline outcomes only | Not reported | Not extracted | Not assessed |
Related questions
The same intervention and outcome, asked in another population or against another comparator.
- Summary of findings table ‐ Convalescent plasma vs standard plasma · outpatients withmild disease · 1 review
How reviews are matched to this question
Reviews word the same question differently. Four decision models (TypeSafe's Jev, Liquid AI's d1-ultra, SubQ Snap and Baseten's Elephant) judged each wording of the population, intervention, comparator and outcome against the others; two wordings count as the same when at least three of them agree and none clearly disagrees, and findings count as this question only when all four elements match. Timing, setting and subgroup never split a question: they are shown on each finding.