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Record W2961757699

Cardioprotective actions of opioids in the ischemic heart: bypassing occlusions in our current knowledge

2019· article· en· W2961757699 on OpenAlexvenueno aff
Peter Johnson

Bibliographic record

VenueUniversity of Toronto Medical Journal · 2019
Typearticle
Languageen
FieldMedicine
TopicCardiac Ischemia and Reperfusion
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMyocardial infarctionStroke (engine)OpioidDiseaseIntensive care medicineAdverse effectCoronary heart diseaseChest painCardiologyInternal medicineReceptor
DOInot available

Abstract

fetched live from OpenAlex

Opioids are extensively utilized therapeutically for management of chest pain during various heart conditions such as, myocardial infarction (MI) and ischemic heart disease (IHD). Emerging literature about the chronic cardiovascular effects of opioid use remains elusive and controversial. While several studies have reinforced the cardioprotective effects of opioid receptor activation in various rodent and animal models, these effects are less established in humans. Other studies have examined the clinical outcomes of opioid use and there is still much division in our understanding. While some of these studies suggest there are no cardiovascular effects, others indicate there are adverse effects including a greater risk for stroke, coronary heart disease and even death. This review paper aims to examine current understandings on the cardiovascular effects of opioids based on experimental and clinical evidence.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.012
GPT teacher head0.288
Teacher spread0.275 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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

Citations0
Published2019
Admission routes1
Has abstractyes

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