General anaesthesia vs non‐general anaesthesia: Functional outcomes
General anaesthesia showed no clear difference in functional outcome (dichotomous; mRS ≤ 2) compared with non‐general anaesthesia (low certainty).
What each review found
Include earlier review versions| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
General anaesthesia vs non‐general anaesthesia Functional outcome (continuous; mRS) · acute ischaemic stroke endovascular treatment Type of anaesthesia for acute ischaemic stroke endovascular treatment (2022) · Cochrane Headline outcomes only | no estimate extracted | 1 study · 90 participants | Very low |
General anaesthesia vs non‐general anaesthesia Functional outcome (dichotomous; mRS ≤ 2) · acute ischaemic stroke endovascular treatment (long‐term) General anaesthesia showed no clear difference in functional outcome (dichotomous; mRS ≤ 2) compared with non‐general anaesthesia (low certainty). Type of anaesthesia for acute ischaemic stroke endovascular treatment (2022) · Cochrane Headline outcomes only | RR 1.21 [0.93, 1.58] | 4 studies · 625 participants | Low |
General anaesthesia vs non‐general anaesthesia Functional outcome (continuous; mRS ≤ 2) · acute ischaemic stroke endovascular treatment (long‐term) Type of anaesthesia for acute ischaemic stroke endovascular treatment (2022) · Cochrane Headline outcomes only | no estimate extracted | 7 studies · 978 participants | Low |
Findings are shown side by side, never pooled across reviews.
The reviews
| Review | Searched up to | Included studies | AMSTAR 2 |
|---|---|---|---|
| Type of anaesthesia for acute ischaemic stroke endovascular treatment 2022 · Cochrane Headline outcomes only | Not reported | list not extracted yet | Not assessed |
Studies behind the findings
None of these reviews ties its included studies to this outcome in the data here. Cochrane reviews and reviews with headline outcomes only do not list their studies yet.
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.