No Cardioprotective Benefit of Ischemic Postconditioning in Patients With <scp>ST</scp>‐<scp>S</scp>egment Elevation Myocardial Infarction
Bibliographic record
Abstract
BACKGROUND: Postconditioning is a potential cardioprotective strategy that has demonstrated conflicting and variable reductions in infarct size in human trials. OBJECTIVES: To determine whether postconditioning could increase the extent of myocardial salvage in patients with acute ST-segment elevation myocardial infarction undergoing primary percutaneous coronary intervention (PPCI). METHODS: One hundred two patients (aged 57 ± 11 years; 88% male) were randomly assigned to a postconditioning or standard protocol. Cardiovascular magnetic resonance imaging was performed 3 days after PPCI to measure the volumetric extent of myocardial necrosis and the area at risk. RESULTS: With similar time-to-reperfusion (170 ± 84 minutes in the postconditioning group vs. 150 ± 70 minutes in the standard group, P = 0.22), the myocardial salvage index was not significantly different between the postconditioned group and the control group, averaging 42 ± 22% vs. 33 ± 21%, respectively (P = 0.08). Furthermore, postconditioning was not associated with a smaller infarct size compared to controls (13 ± 7 g/m(2) vs. 15 ± 8 g/m(2), respectively, P = 0.18). CONCLUSIONS: Postconditioning does not significantly increase myocardial salvage or reduce infarct size in patients with STEMI undergoing PPCI. However, the possibility of a more modest impact of postconditioning cannot be excluded with our sample size.
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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 | 0.000 | 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".