Effects of remote ischemic preconditioning in high-risk patients undergoing cardiac surgery (Remote IMPACT): a randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Remote ischemic preconditioning is a simple therapy that may reduce cardiac and kidney injury. We undertook a randomized controlled trial to evaluate the effect of this therapy on markers of heart and kidney injury after cardiac surgery. METHODS: Patients at high risk of death within 30 days after cardiac surgery were randomly assigned to undergo remote ischemic preconditioning or a sham procedure after induction of anesthesia. The preconditioning therapy was three 5-minute cycles of thigh ischemia, with 5 minutes of reperfusion between cycles. The sham procedure was identical except that ischemia was not induced. The primary outcome was peak creatine kinase-myocardial band (CK-MB) within 24 hours after surgery (expressed as multiples of the upper limit of normal, with log transformation). The secondary outcome was change in creatinine level within 4 days after surgery (expressed as log-transformed micromoles per litre). Patient-important outcomes were assessed up to 6 months after randomization. RESULTS: We randomly assigned 128 patients to remote ischemic preconditioning and 130 to the sham therapy. There were no significant differences in postoperative CK-MB (absolute mean difference 0.15, 95% confidence interval [CI] -0.07 to 0.36) or creatinine (absolute mean difference 0.06, 95% CI -0.10 to 0.23). Other outcomes did not differ significantly for remote ischemic preconditioning relative to the sham therapy: for myocardial infarction, relative risk (RR) 1.35 (95% CI 0.85 to 2.17); for acute kidney injury, RR 1.10 (95% CI 0.68 to 1.78); for stroke, RR 1.02 (95% CI 0.34 to 3.07); and for death, RR 1.47 (95% CI 0.65 to 3.31). INTERPRETATION: Remote ischemic precnditioning did not reduce myocardial or kidney injury during cardiac surgery. This type of therapy is unlikely to substantially improve patient-important outcomes in cardiac surgery. TRIAL REGISTRATION: ClinicalTrials.gov, no. NCT01071265.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".