MétaCan
Menu
Back to cohort
Record W4406614220 · doi:10.1213/ane.0000000000007357

Comparing General Anesthesia–Based Regimens for Endovascular Treatment of Acute Ischemic Stroke: A Systematic Review and Network Meta-Analysis

2025· review· en· W4406614220 on OpenAlexaff
Eric Plitman, Ayman A. Mohammed, Wesley Rajaleelan, Rodrigo Nakatani, Marina Englesakis, Jai Shankar, Lashmi Venkatraghavan, Tumul Chowdhury

Bibliographic record

VenueAnesthesia & Analgesia · 2025
Typereview
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsUniversity of ManitobaUniversity of OttawaToronto Western HospitalUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineOdds ratioAnesthesiaModified Rankin ScaleSedationConfidence intervalStroke (engine)MEDLINEInternal medicineIschemic strokeIschemia

Abstract

fetched live from OpenAlex

BACKGROUND: Total intravenous anesthesia (TIVA)-based and volatile-based general anesthesia have different effects on cerebral hemodynamics. The current work compares these 2 regimens in acute ischemic stroke patients undergoing endovascular therapy. METHODS: We conducted a systematic literature search across MEDLINE, Embase, Cochrane, CINAHL, Web of Science, and Scopus. We identified English language studies including adult acute ischemic stroke patients managed with endovascular therapy under general anesthesia delineable into TIVA only and/or volatile only, and obtained categorical data for favorable functional outcomes using the modified Rankin scale (mRS ≤2), at 90 days after endovascular therapy. Odds ratios (OR) and standardized mean differences were calculated to inform a network meta-analysis approach, which permitted the inclusion of studies comparing a form of general anesthesia (ie, TIVA only or volatile only) to conscious sedation. RESULTS: The search rendered 6235 articles, of which 15 met inclusion criteria. Three studies directly investigated TIVA versus volatile, whereas 12 studies compared general anesthesia to conscious sedation. The total number of subjects was 3015 (conscious sedation: n = 1067; general anesthesia: n = 1948 [TIVA: n = 1212, volatile: n = 736]). No significant differences were identified between TIVA and volatile groups in 90-day neurological outcome (OR = 1.25, 95% confidence interval [CI], 0.81-1.91; P = .31), 90-day mortality (OR = 0.72, 95% CI, 0.42-1.24; P = .24), successful recanalization (OR = 1.33, 95% CI, 0.70-2.52; P = .39), or recanalization time (standardized mean difference = 0.03, 95% CI, -0.35 to 0.41; P = .88). Additionally, no significant differences were identified between the conscious sedation group and the TIVA group in 90-day neurological outcome (OR = 1.14, 95% CI, 0.84-1.53; P = .40), 90-day mortality (OR = 0.87, 95% CI, 0.62-1.23; P = .43), successful recanalization (OR = 0.76, 95% CI, 0.52-1.10; P = .15), or recanalization time (standardized mean difference = -0.18, 95% CI, -0.47 to 0.11; P = .23), and between the conscious sedation group and the volatile group in 90-day neurological outcome (OR = 1.42, 95% CI, 0.92-2.17; P = .11), 90-day mortality (OR = 0.63, 95% CI, 0.36-1.12; P = .11), successful recanalization (OR = 1.01, 95% CI, 0.52-1.94; P = .98), or recanalization time (standardized mean difference = -0.15, 95% CI, -0.52 to 0.23; P = .44). CONCLUSIONS: This network meta-analysis showed that the perioperative use of either general anesthesia-based regimen, or sedation, did not significantly impact various endovascular therapy-related outcomes. However, the current work was underpowered to detect differences in anesthetic agents, clinico-demographic characteristics, or procedural factors.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.016
metaresearch head score (Gemma)0.042
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.019
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.042
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0190.036
Bibliometrics0.0080.008
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.062
GPT teacher head0.326
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations3
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueAnesthesia & AnalgesiaSame topicAcute Ischemic Stroke ManagementFrench-language works237,207