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Cardiac response to hemodialysis with different cardiovascular tolerance: Heart rate variability and QT interval analysis

2006· article· en· W2060764158 on OpenAlexvenueno aff
Stefano Severi, Andrea Ciandrini, Eleonora Grandi, Silvio Cavalcanti, Stefano A. Bini, Fabio Badiali, A Gattiani, L Cagnoli

Bibliographic record

VenueHemodialysis International · 2006
Typearticle
Languageen
FieldMedicine
TopicHeart Rate Variability and Autonomic Control
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHemodialysisQT intervalCardiologyHeart rate variabilityInternal medicineDialysisHeart rateNitric oxideBlood pressure

Abstract

fetched live from OpenAlex

A therapy-specific worsening of cardiovascular stability during bicarbonate dialysis (BD) with respect to acetate-free biofiltration (AFB) have been previously reported. We further investigated the impact of the 2 therapies on electrocardiographic parameters in order to gain novel insight into the cardiac responses. Holter ECG acquired during hypotension-free sessions (12 BD + 12 AFB) were retrospectively analyzed. R-R intervals were extracted from ECG recordings. An autoregressive spectral technique was used to compute low- and high-frequency (LF and HF) components of heart rate variability (HRV). QT interval duration was measured with a computer-assisted technique and corrected for HR. In BD the LF component of HRV after an initial increase was slowly depressed with respect to AFB (p < 0.05). QT duration showed a significant (p < 0.01) hemodialysis-induced reduction. QT shortening was more pronounced (p < 0.05) in BD than in AFB (-31 vs. -10 ms), even after correction for HR (p < 0.05). Cardiac electrical activity is significantly affected by the hemodialysis technique. The decrease in the LF component of HRV and the QT shortening are coherent with the worse cardiovascular tolerance observed in BD and with the hypothesis of an enhanced production of endogenous nitric oxide.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.127
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
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 head0.008
GPT teacher head0.238
Teacher spread0.230 · 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

Citations23
Published2006
Admission routes1
Has abstractyes

Explore more

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