Neuroprotection in aortic arch surgery: a meta-analysis of hypothermia and selective cerebral perfusion on perioperative stroke and cognitive outcomes
Bibliographic record
Abstract
Background: Hypothermic circulatory arrest is an essential aspect of aortic arch surgery and is classically used in severe hypothermia (≤20 °C) to reduce cerebral metabolic demand. However, severe hypothermia is associated with systemic problems. Recently, moderate hypothermia (20–28 °C) combined with selective cerebral perfusion (SCP) has been proposed as an option, which may reduce the risk of adverse neurological consequences. This study aims to see whether this newer approach confers any neuroprotective benefits. Methods: A comprehensive literature search was conducted on PubMed, Embase, Cochrane Library, Web of Science, and Scopus for English-language studies published in the last 20 years. Eligible studies comparing deep hypothermia with moderate hypothermia with SCP in adult patients after aortic arch surgery, with a focus on the incidence of perioperative stroke and neurocognitive outcomes. Data collection and quality assessment were performed using the Cochrane Risk of Bias Tool for Randomized Clinical Trials and the Newcastle-Ottawa Scale for observational studies. A meta-analysis of randomized clinical trials was performed using RevMan software. Results: Six studies (four randomized controlled trials and two observational studies) met the inclusion criteria. Pooled analysis indicated that deep hypothermia was associated with a significantly increased risk of perioperative stroke compared with moderate hypothermia (relative risk, 1.74; 95% confidence interval, 1.30–2.35), but heteogeneity ( I 2 = 0%). The funnel plot revealed a sand-synthesized model with minimal publication bias. Conclusion: Moderate hypothermia with SCP provides better neuroprotection than deep hypothermia in aortic arch surgery, which significantly reduces the incidence of perioperative stroke. Further in-depth studies are needed to validate these findings.
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.057 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".