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Cardiac autonomic dysfunction in hemodialysis patients: The value of heart rate turbulence

2011· article· en· W1872051371 on OpenAlexvenueno aff
Ataç Çelik, Mehmet Melek, Şeref Yüksel, Ersel Onrat, Alaettin Avşar

Bibliographic record

VenueHemodialysis International · 2011
Typearticle
Languageen
FieldMedicine
TopicHeart Rate Variability and Autonomic Control
Canadian institutionsnot available
Fundersnot available
KeywordsHeart rate turbulenceMedicineHeart rate variabilityHemodialysisCardiologyInternal medicineHeart rateSudden cardiac deathPopulationAmbulatoryAutonomic nervous systemBlood pressure

Abstract

fetched live from OpenAlex

Patients with end-stage renal disease (ESRD) are likely to have cardiac autonomic dysfunction, which is related with an increased risk of sudden death. The aim of this study is to detect cardiac autonomic dysfunction in patients with ESRD and to evaluate the possible acute effects of hemodialysis (HD) on cardiac autonomic functions measured by heart rate variability (HRV) and heart rate turbulence (HRT). Thirty-one (mean age 50 ± 13 years, 15 males) with ESRD on regular HD program and 31 healthy volunteers (mean age 51 ± 12 years, 15 males) were included in the study. Twenty-four-hour ambulatory electrocardiogram recordings were taken from the subjects before and after HD and from the control group. Heart rate variability and HRT parameters were calculated from these recordings. All of the HRV and HRT parameters were found to be significantly blunted in patients in comparison with healthy individuals. There were significant differences in HRV after HD, but similar differences were not observed in HRT parameters. Cardiac autonomic functions were significantly altered in patients with ESRD. Heart rate turbulence parameters seemed to be less affected from HD and may be more useful in the evaluation of cardiac autonomic functions in the ESRD population.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.861

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.236
Teacher spread0.221 · 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

Citations24
Published2011
Admission routes1
Has abstractyes

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