Local versus general anesthesia for endovascular aneurysm repair in ruptured abdominal aortic aneurysm: A systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract Objectives/Background In the endovascular treatment of ruptured abdominal aortic aneurysm (RAAA), there is no effective evidence to show preference for a specific anesthetic option. A meta‐analysis was conducted to assess the result of different anesthesia in endovascular aneurysm repair (EVAR) of RAAA. Methods Randomized controlled trials (RCTs) and cohort studies were searched in PubMed, Embase, Ovid, and the Cochrane Library. Newcastle‐Ottawa Scale and the Cochrane Risk of Bias Tool were applied to evaluate the quality of cohort studies and RCTs, respectively. Odds ratios (ORs) and 95% confidence intervals (CIs) were used to express differences for primary and secondary outcomes. Subgroup analyses and sensitivity analyses were applied in the primary outcome to illustrate the results further. Significance was set at p < 0.05. Random‐effects models were used considering limited research regardless of I2 < 50%. Results Ten cohort studies were included in this meta‐analysis. Perioperative mortality was presented as the primary outcome by analyzing eight of these research. Among the included patients, local anesthesia (LA) was considered as a better choice considering perioperative mortality (n = 156/902) rather than general anesthesia (n = 907/3434) with significant difference (OR: 0.49; 95% CI: 0.35−0.67; p < 0.00001; I2 = 42%). However, no significant difference was found in the secondary outcome: the complication rate, ICU admission rate, postoperative morbidity of pneumonia, myocardial infarction, leg ischemia, and wound complication. Conclusions There exists some evidence in this review that LA appears to improve perioperative mortality, especially in hemodynamically stable patients and should be recommended for patients undergoing EVAR with RAAA when appropriate.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.038 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".