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Record W1758607685 · doi:10.7717/peerj.1354

Frailty severity is significantly associated with electrocardiographic QRS duration in chronic dialysis patients

2015· article· en· W1758607685 on OpenAlexaboutno aff
Chia‐Ter Chao, Jenq‐Wen Huang

Bibliographic record

VenuePeerJ · 2015
Typearticle
Languageen
FieldMedicine
TopicCardiac pacing and defibrillation studies
Canadian institutionsnot available
Fundersnot available
KeywordsQRS complexInternal medicineMedicineCardiologyDialysisDuration (music)

Abstract

fetched live from OpenAlex

End-stage renal disease (ESRD) patients are at increased risk of sudden cardiac death, the risk of which is presumably related to arrhythmia. Electrocardiographic (ECG) parameters have been found to correlate with arrhythmia and predict cardiovascular outcomes in ESRD patients. Frailty is also a common feature in this population. We investigate whether the severity of dialysis frailty is associated with ECG findings, including PR interval, QRS duration, and QTc interval. Presence and severity of frailty was ascertained using six different self-report questionnaires with proven construct validity. Correlation analysis between frailty severity and ECG was made, and those with significant association entered into multiple regression analysis for confirmation. Among a cohort of chronic hemodialysis patients, we found that frailty severity, assessed by the Edmonton frailty scale, is significantly associated with QRS duration (r = - 0.3, p < 0.05). Dialysis patients with QRS longer than 120 ms had significantly lower severity of frailty than those with QRS less than 120 ms (p = 0.01 for the Edmonton frailty scale and 0.05 for simple FRAIL scale). Regression analysis showed that frailty severity, assessed by the Edmonton frailty scale and simple FRAIL scale, was significantly associated with QRS duration independent of serum electrolyte levels. In conclusion, a significant relationship exists between the severity of frailty and QRS duration in ESRD patients. This might be an under-recognized link between frailty and its adverse cardiovascular impact in these patients.

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)0.000
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 score0.012
Threshold uncertainty score0.422

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.031
GPT teacher head0.276
Teacher spread0.245 · 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

Citations25
Published2015
Admission routes1
Has abstractyes

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