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Record W2620884248 · doi:10.4172/2368-0512.1000074

The role of the sympathetic nervous system in sudden cardiac death

2016· article· en· W2620884248 on OpenAlexaffvenue
Ajaipal S Randhawa, Ravideep S. Dhadial

Bibliographic record

VenueCurrent research. Cardiology · 2016
Typearticle
Languageen
FieldMedicine
TopicCardiac electrophysiology and arrhythmias
Canadian institutionsUniversity of ManitobaSt. Boniface Hospital
FundersVedecká Grantová Agentúra MŠVVaŠ SR a SAVAmerican Heart Association
KeywordsSympathetic nervous systemSudden cardiac deathMedicineAutonomic nervous systemCardiologyInternal medicineHeart rateBlood pressure

Abstract

fetched live from OpenAlex

Sudden cardiac death (SCD) is a complex disease and represents one of the largest burdens to health care worldwide.It is known to be associated with various disease conditions, including but not limited to, cardiac arrhythmias, coronary artery disease, cardiomyopathies, valvular disorders and diabetes.Although much work is left to be done in uncovering the underlying mechanisms of SCD, response of the sympathetic nervous system to a prolonged stressful stimulus and the release of excessive amounts of catecholamines and their subsequent oxidation has potential to explain the regularly observed etiologies and provide a unified mechanism for the occurrence of SCD.Current possible treatments for patients at risk for SCD range from the implantation of cardioverter defibrillators to pharmaceutical interventions including anti-arrhythmic drugs, antiplatelet agents and β-adrenoceptor blockers.However, there are some studies suggesting the merit of prophylactic treatment using antioxidants such as vitamins A, C and E in preventing arrhythmias and consequent SCD.Overstimulation of the sympathetic stress response may result in SCD, and combination therapy with antioxidants and β-adrenoceptor blockers may be suitable for its prevention.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.411
Threshold uncertainty score0.302

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.029
GPT teacher head0.328
Teacher spread0.299 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations4
Published2016
Admission routes2
Has abstractyes

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