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The role of dietary cations in the blood pressure of renal transplant recipients

2005· article· en· W2024643377 on OpenAlexaff
G. V. Ramesh Prasad, Michael Huang, Michelle M. Nash, Jeffrey S. Zaltzman

Bibliographic record

VenueClinical Transplantation · 2005
Typearticle
Languageen
FieldNursing
TopicSodium Intake and Health
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsBlood pressureExcretionMedicineInternal medicineTransplantationPopulationEndocrinologyRenal transplantUrineUrology

Abstract

fetched live from OpenAlex

BACKGROUND: The role of dietary cations in hypertension has been evaluated in the general population and selected subgroups, but its contribution to blood pressure (BP) elevations in patients with functional renal allografts has not been critically examined. METHODS: After counseling based on Dietary Approaches to Stop Hypertension (DASH) guidelines, we measured timed 24-h urine excretion rates of sodium, potassium, calcium, and magnesium as a surrogate for their dietary intake, in 244 stable adult single-organ renal transplant recipients, correlating these with averaged blinded clinic-measured BP values. Multiple linear regression analysis adjusting for factors affecting BP in transplant recipients was performed. RESULTS: There was no correlation between systolic (SBP) or diastolic pressure (DBP) and 24-h urine excretion rates of each cation. There was no BP difference between patients receiving cyclosporine and tacrolimus (127/77 vs. 129/78 mmHg, p = 0.38), or in cation excretion except for calcium (2.85 +/- 2.0 vs. 2.90 +/- 2.8, p = 0.002). Protein excretion (p < 0.0001), age (p = 0.002), and weight (p = 0.04) were positively associated with SBP, while only weight (p = 0.01) was associated with DBP by multivariate analysis. CONCLUSION: Dietary cation intake is not significantly associated with BP in renal transplant recipients. These data do not support recommendations to alter dietary cation intake as part of the management of post-transplantation hypertension.

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 score0.232
Threshold uncertainty score0.189

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)0.0000.000

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 head0.050
GPT teacher head0.370
Teacher spread0.320 · 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

Citations15
Published2005
Admission routes1
Has abstractyes

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