Coronary microvascular reperfusion injury and noreflow in acute myocardial infarction
Bibliographic record
Abstract
PURPOSE: To review (1) the mechanisms of coronary microvascular reperfusion injury, particularly in the relationships between microvascular endothelium dysfunction, microstructure damage, microemboli and no-reflow phenomena; (2) the no-reflow presentation and management at ischemia-reperfusion to suggest future direction for no-reflow therapy in acute myocardial infarction. SOURCES: Original articles and reviews published between 1997 and 2007 and focusing on the no-reflow phenomenon in MEDLINE and PubMed. The search terms used were "no-reflow", "microvascular injury", "acute myocardial infarction" and "reperfusion injury". All papers identified were English-language, full text papers. In addition, the reference lists of identified relevant articles were also searched. CONCLUSIONS: The no-reflow phenomenon is characterised by damage to microvascular function and microstructure at ischaemia-reperfusion. Microemboli contribute to no-reflow. Clinical myocardial contrast echocardiography (MCE), scintigraphic and magnetic resonance imaging (MRI) have shown evidence of microvascular damage, eg, perfusion defects are closely related to lack of contractile recovery and irreversible myocyte damage. Clinical agents and devices targeting microvascular injury (especially protection of endothelium and reduction of microemboli) after acute myocardial infarction may be key points to improve no-reflow.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".