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Record W1214355284 · doi:10.4172/2329-6607.1000e127

Therapeutic Potential of Adenosine Transport Modulators for Cardiovascular Protection

2015· article· en· W1214355284 on OpenAlexaff
Shyam Sundar Kolathuru

Bibliographic record

VenueCardiovascular Pharmacology Open Access · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdenosine and Purinergic Signaling
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMedicineStroke (engine)DiseaseMyocardial infarctionIntensive care medicineCause of deathBurden of diseaseCoronary heart diseaseHealth careEmergency medicineInternal medicine

Abstract

fetched live from OpenAlex

Cardiovascular disease including stroke is the leading cause of death and disability worldwide [1] and an enormous economic burden to our societies [2]. Based on the latest statistics released for heart and stroke disease, an estimated 83,600,000 adults in the United State (US) (>30%) have one or more types of cardiovascular disease (CVD) of whom more than 90% have hypertension, 18% have coronary heart disease (CHD), close to 10% have myocardial infarction (MI) and 8% have stroke. The total direct and indirect cost in the US alone for treatment of cardiovascular diseases (hospitalization, drugs, home healthcare, etc.) and loss of productivity and morbidity is estimated at close to $315 billion US per year [3]. Thus prevention by improving diagnosis and drug treatment strategies could provide a huge saving for the health care cost worldwide.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.081
GPT teacher head0.357
Teacher spread0.276 · 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

Citations2
Published2015
Admission routes1
Has abstractyes

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