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Record W4280624499 · doi:10.3389/fphar.2022.856747

Adenosine in Acute Myocardial Infarction-Associated Reperfusion Injury: Does it Still Have a Role?

2022· review· en· W4280624499 on OpenAlexaff
Corrado De Marco, Thierry Charron, Guy Rousseau

Bibliographic record

VenueFrontiers in Pharmacology · 2022
Typereview
Languageen
FieldMedicine
TopicCardiac Ischemia and Reperfusion
Canadian institutionsUniversité de MontréalCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalHôpital du Sacré-Cœur de Montréal
Fundersnot available
KeywordsMedicineMyocardial infarctionAdenosineMyocardial Reperfusion InjuryReperfusion injuryCardiologyCardioprotectionInfarctionInternal medicinePharmacologyAnesthesiaIschemia

Abstract

fetched live from OpenAlex

The mainstay of acute myocardial infarction has long been timely reperfusion of the culprit obstruction. Reperfusion injury resulting from a multitude of pathophysiological processes has been demonstrated to negatively affect myocardial recovery and function post-infarction. Adenosine interacts directly with the sequential pathophysiological processes culminating in reperfusion injury by inhibiting them upstream. The evidence for adenosine's benefit in acute myocardial infarction has produced mixed results with regards to myocardial salvage and long-term mortality. The heterogenous evidence with regards to benefits on clinical outcomes has resulted in modest uptake of adenosine in the clinical setting. However, it is critical to analyze the variability in study methodologies. The goal of this review is to evaluate how adenosine dose, route of administration, timing of administration, and site of administration play essential roles in the molecule's efficacy. The benefits of adenosine, as highlighted in the following review, are clear and its role in the treatment of acute myocardial infarction should not be discounted.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.671
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.015
GPT teacher head0.331
Teacher spread0.317 · 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 designNot applicable
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

Citations15
Published2022
Admission routes1
Has abstractyes

Explore more

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