Sustained nicorandil administration reduces infarct size in ST-segment elevation myocardial infarction patients with primary percutaneous coronary intervention
Bibliographic record
Abstract
OBJECTIVE: Currently, there is still no effective strategy to diminish the infarct size (IS) in patients with ST-segment elevation myocardial infarction (STEMI). According to a previous animal study, nicorandil treatment is a promising pharmaceutical treatment to limit the infarct area. In this study, we aim to investigate the effects of continual nicorandil administration on the IS and the clinical outcomes in patients with STEMI who underwent primary percutaneous coronary intervention (pPCI). METHODS: One hundred seventeen patients with STEMI and undergoing pPCI were randomly divided into the sustained nicorandil group (5 mg, three times daily) or the control group (only single nicorandil before PCI). The primary endpoint was the IS, evaluated by single-photon emission computed tomography (SPECT) 3 months after pPCI. RESULTS: Eighty-five patients completed the IS assessment via SPECT, and 99 participants were available for follow-up after 6 months. Finally, there was a statistical difference in the IS between the nicorandil and control groups {13% [interquartile range (IQR), 8-17] versus 16% [IQR, 12-20.3], p=0.027}. Additionally, we observed that maintained nicorandil administration significantly improved the left ventricular ejection fraction at 3 months and enhanced the activity tolerance (physical limitation and angina stability) at 6 months after PCI. CONCLUSION: Sustained nicorandil treatment reduced the IS and improved the clinical outcomes compared to the single nicorandil administration for patients with STEMI undergoing the pPCI procedure. Continuous cardioprotective therapy may be more beneficial for patients with STEMI.
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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) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".