Question · asked by 0 reviews
Vitamin K antagonist (VKA) prophylaxis vs no VKA: Quality of life ‐ not reported
- Population
- people with cancer with thrombosis prophylaxis and central venous catheters
- Intervention
- Vitamin K antagonist (VKA) prophylaxis
- Comparison
- no VKA
- Outcome
- Quality of life ‐ 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 |
|---|---|---|---|
VKA vs no VKA Quality of life ‐ not reported · patients with thrombosis prophylaxis in cancer patients with central venous catheters The review found no eligible studies for this question. Anticoagulation for people with cancer and central venous catheters (2011) · Cochrane Headline outcomes onlyearlier version CD006468.PUB3, superseded | no estimate extracted | Not reported | No studies found |
VKA vs no VKA Quality of life ‐ not reported · patients with thrombosis prophylaxis in cancer patients with central venous catheters The review found no eligible studies for this question. Anticoagulation for people with cancer and central venous catheters (2011) · Cochrane Headline outcomes onlyearlier version CD006468.PUB4, superseded | no estimate extracted | Not reported | No studies found |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 |
|---|---|---|---|
| Anticoagulation for people with cancer and central venous catheters 2018 · 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.
- Vitamin K antagonist (VKA) prophylaxis vs no prophylaxis · ambulatory people with cancer without venous thromboembolism receiving systemic therapy · 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.