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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.054 | 0.006 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".