IMproving Preclinical Assessment of Cardioprotective Therapies (IMPACT): a small animal acute myocardial infarction randomized-controlled multicenter study on the effect of ischemic preconditioning
Bibliographic record
Abstract
Although many cardioprotective interventions have been shown to limit infarct size (IS), in preclinical animal studies of acute myocardial ischemia/reperfusion injury (IRI), their clinical translation to patient benefit has been largely disappointing. A major factor is the lack of rigor and reproducibility in the preclinical studies. To address this, we have established the IMproving Preclinical Assessment of Cardioprotective Therapies (IMPACT) small animal multisite acute myocardial infarction (AMI) network, with centralized randomization and blinded core laboratory IS analysis, and have validated the network using ischemic preconditioning (IPC). Eight sites from the COST Innovators Grant (IG16225) network participated in the IMPACT AMI study. Mice and rats were randomly allocated into Sham, Control, or IPC groups. The IRI group underwent 45 min (mice) or 30 min (rats) of left coronary artery occlusion followed by 24 h reperfusion. IPC comprised three cycles of 5 min occlusion/reperfusion before IRI. IS was determined by a blinded core lab. The majority of site showed significant cardioprotection with IPC. In pooled mouse data, IPC (N = 42) reduced IS/AAR by 35% compared to control (N = 48) (30 ± 16% versus 46 ± 13%; p < 0.005), and in rat data, IPC (N = 36) reduced IS/AAR by 29% when compared to control (N = 39) (32 ± 19% versus 45 ± 14%; p < 0.01). The IMPACT multisite mouse and rat AMI networks, with centralized randomization and blinded core IS analysis, were established to improve the reproducibility of cardioprotective interventions in preclinical studies and to facilitate the translation of these therapies for patient benefit.
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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 | 0.016 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".