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Record W2102083961 · doi:10.25011/cim.v30i3.1082

Coronary microvascular reperfusion injury and noreflow in acute myocardial infarction

2007· review· en· W2102083961 on OpenAlexvenueno aff
Sheng Kang, Yuejin Yang

Bibliographic record

VenueClinical and investigative medicine · 2007
Typereview
Languageen
FieldMedicine
TopicCardiac Ischemia and Reperfusion
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCardiologyMyocardial infarctionNo reflow phenomenonInternal medicineIschemiaPerfusionReperfusion injuryMagnetic resonance imagingInfarctionRadiologyPercutaneous coronary intervention

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.981
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.001
Science and technology studies0.0000.007
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.131
GPT teacher head0.405
Teacher spread0.273 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations34
Published2007
Admission routes1
Has abstractyes

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