Cerebral protection by remote ischemic post-conditioning in patients with ischemic stroke: A systematic review and meta-analysis of randomized controlled trials
Bibliographic record
Abstract
Background There is evidence that remote limb ischemic postconditioning (RIPostC) can reduce ischemia-reperfusion injury (IRI) and improve the prognosis of patients with ischemic stroke. However, so far, only few relevant clinical studies have been conducted. Therefore, we carried out a meta-analysis of eligible randomized controlled trials to compare the RIPostC group with a control group (no intervention or sham surgery) in patients with ischemic stroke. Methods Four English-language publication databases, PubMed, Cochrane, Embase, and Web of Science, were systematically searched up to March 2022. The data were analyzed using Review Manager fixed-effects and random-effects models. Results A total of 12 studies were included, and 11 of those were analyzed quantitatively. Compared to controls, The RIPostC group showed significantly reduced NIHHS scores in patients with ischemic stroke, (MD: −1.09, 95% confidence interval [CI]: −1.60, −0.57, P < 0.0001) and improved patients' Montreal Cognitive Assessment (MoCA) scores, (MD: 1.89, 95% CI: 0.78, 3.00, P = 0.0009), Our results showed that RIPostC is safe, (RR = 0.81, 95%CI: 0.61, 1.08, P = 0.15). Conclusion Our meta-analysis showed that RIPostC is safe and effective and has a positive cerebral protective effect in patients with ischemic stroke, which is safe and effective, and future large-sample, multicenter trials are needed to validate the cerebral protective effect of RIPostC.
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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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.035 |
| Bibliometrics | 0.007 | 0.007 |
| 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".