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Record W2560720813 · doi:10.25011/cim.v39i6.27519

Autonomic Cardiac Activity in Patients with Smoking and Alcohol Addiction by Heart Rate Variability Analysis

2016· article· en· W2560720813 on OpenAlexvenueno aff
Ramazan Yüksel, Rabia Nazik Yüksel, Tijen Şengezer, Şenol Dane

Bibliographic record

VenueClinical and investigative medicine · 2016
Typearticle
Languageen
FieldMedicine
TopicHeart Rate Variability and Autonomic Control
Canadian institutionsnot available
Fundersnot available
KeywordsHeart rate variabilityAddictionMedicineNicotineNicotine AddictionAlcoholCigarette smokingCardiologyInternal medicineHeart rateAnesthesiaPsychiatryBlood pressure

Abstract

fetched live from OpenAlex

Purpose Smoking and alcohol addictions are common and worldwide. In the present study, we aimed to investigate the effects of these addictions on cardiac rhythm using heart rate variability (HRV) analysis. Methods Addicts (n=42 men: 22 cigarette; 20 cigarette and alcohol) and age-matched controls (n=34 men) were included in the study. All patients fulfill the criteria for dependence according to DSM-IV-TR. Electrocardiography (ECG) recordings were obtained for a total of 30 minutes. Fagerstrom Nicotine Addiction Test (FNAT) and CAGE questionnaire (Cut down, Annoy, Guilt, Eye opener) was applied to all patients. Results Almost all HRV parameters were significantly decreased in cigarette and cigarette and alcohol addicts compared with controls (p<0.05). The mean heart rate (bpm) increased in both addict groups compared with control group, and this increase was particularly significant in cigarette and alcohol addicts. Conclusions The cardiac autonomic balance shifted in favor of sympathetic activity by suppressing the parasympathetic activity in addicts; therefore, the present study shows that smoking and/or alcohol addiction leads to sympathetic activation and parasympathetic inhibition. Reduced vagal activity also predisposes to cardiac arrhythmias. This suggests an increased risk of cardiovascular mortality in subjects with smoking and alcohol addiction.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.004
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.052
GPT teacher head0.304
Teacher spread0.252 · 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 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

Citations28
Published2016
Admission routes1
Has abstractyes

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