Question · asked by 1 review
Prophylactic plasma transfusion before surgery/invasive procedures vs no prophylactic plasma transfusion before surgery/invasive procedures (colloid, crystalloid, placebo, or no treatment): Quality of life ‐ not reported
- Population
- patients undergoing non‐cardiac surgery/invasive procedures
- Intervention
- Prophylactic plasma transfusion before surgery/invasive procedures
- Comparison
- no prophylactic plasma transfusion before surgery/invasive procedures (colloid, crystalloid, placebo, or no treatment)
- Outcome
- Quality of life ‐ not reported
The review found no eligible studies for this question.
What each review found
Current versions only| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Prophylactic plasma transfusion before surgery/invasive procedures vs no prophylactic plasma transfusion before surgery/invasive procedures (colloid, crystalloid, placebo, or no treatment) Quality of life ‐ not reported · patients undergoing non‐cardiac surgery or invasive procedures The review found no eligible studies for this question. Prophylactic plasma transfusion for patients without inherited bleeding disorders or anticoagulant use… (2019) · 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 |
|---|---|---|---|
| Prophylactic plasma transfusion for patients without inherited bleeding disorders or anticoagulant use undergoing non‐cardiac surgery or… 2019 · 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.
- Prophylactic plasma transfusion before surgery/invasive procedures vs alternative haemostatic agents · patients undergoing non‐cardiac surgery/invasive procedures · 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.