Remote Ischemic Postconditioning During Percutaneous Coronary Interventions
Bibliographic record
Abstract
BACKGROUND: Remote ischemic preconditioning may result in reduction in infarct size during percutaneous coronary intervention (PCI). It is unclear whether remote ischemic postconditioning (RIPost) will reduce the incidence of myocardial injury after PCI, and whether ischemic conditioning of a larger remote organ (thigh versus arm) would provide further myocardial protection. METHODS AND RESULTS: We randomized 360 patients presenting with stable or unstable angina (28% of patients) and negative Troponin T at baseline to 3 groups: 2 groups received RIPost (induced by ischemia to upper or lower limb), and a third was the control group. RIPost was applied during PCI immediately after stent deployment, by three 5-minute cycles of blood pressure cuff inflation to >200 mm Hg in the arm or thigh (20 mm Hg in the control) with 5-minute breaks between each cycle. The primary end-point was the proportion of patients with Troponin T levels >3×ULN postprocedure (at 6 or 18-24 hours), where ULN stands for upper limit of normal. A total of 120 patients were randomized to each group. There were no differences in baseline characteristics between the 3 groups. The primary outcome occurred in 30%, 35%, and 35% of the arm, thigh, and control groups, respectively (P=0.64). There were no differences in creatine kinase or high sensitivity C-reactive protein levels after PCI or in the incidence of acute kidney injury between the groups. CONCLUSIONS: RIPost during PCI did not reduce the incidence of periprocedural myocardial injury. Similar effect was obtained when remote ischemia was induced to the upper or lower limb. CLINICAL TRIAL REGISTRATION URL: http://www.clinicaltrials.gov. Unique identifier: NCT00970827.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".