Remote ischaemic preconditioning and survival in noncardiac surgery: a meta-analysis of randomised trials
Bibliographic record
Abstract
BACKGROUND: Remote ischaemic preconditioning (RIPC) is an intervention involving brief periods of limb ischaemia to protect remote organs from subsequent ischaemic injury. Although evidence exists on the beneficial effects of RIPC on biomarkers, its effect on survival is unknown. We performed a meta-analysis of randomised controlled trials (RCTs) to evaluate whether RIPC improves survival in noncardiac surgery. METHODS: We searched several electronic databases for randomised trials comparing RIPC vs a control group in adult noncardiac surgical settings. The primary outcome was mortality at the longest follow-up available. We conducted a random-effects meta-analysis to calculate the risk ratio (RR) and 95% confidence intervals (CIs). Bayesian statistics were used to estimate the probability of mortality benefit (RR <1). RESULTS: =0%; moderate certainty; number needed to treat = 67), corresponding to a 97.0% probability of any reduction in mortality. RIPC was also associated with a reduced incidence of postoperative stroke (moderate certainty) and with a shorter duration of hospital stay (low certainty). CONCLUSIONS: Remote ischaemic preconditioning was associated with improved survival and reduced postoperative stroke and hospital stay in noncardiac surgery. These findings warrant careful considerations of the benefits of RIPC and support the need for a large, multicentre RCT to confirm these promising results. SYSTEMATIC REVIEW PROTOCOL: CRD42024588358 (PROSPERO).
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.023 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".