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Twenty four-hour ambulatory blood pressure profiles 12 months post living kidney donation

2010· article· en· W1568451674 on OpenAlexaff
G. V. Ramesh Prasad, Deborah Lipszyc, Sulagna Sarker, Michael Huang, Michelle M. Nash, Lindita Rapi

Bibliographic record

VenueTransplant International · 2010
Typearticle
Languageen
FieldMedicine
TopicOrgan Donation and Transplantation
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsMedicineCreatinineAmbulatoryAmbulatory blood pressureUrineBlood pressureRenal functionInternal medicineUrologyProspective cohort studyKidneyEndocrinologyGastroenterology

Abstract

fetched live from OpenAlex

Summary Small blood pressure (BP) elevations may occur post kidney donation. This prospective study determined 24-h ambulatory BP (ABP) and other cardiovascular risk factor changes in 51 living donors over 12 months postdonation. Donors also provided 24-h urine collections for monitoring protein and creatinine clearance, 75 g oral glucose tolerance tests (OGTT), and fasting lipids. Nondipping was defined as night-day systolic (SBP) ratio >or=0.9. Baseline and 12-month pre to postdonation comparisons were made both for dippers and nondippers. Of 51 donors, 35 were dippers and 16 nondippers. In these two groups, predonation 24-h SBP were 115.2 +/- 8 and 115.6 +/- 10 mmHg; serum creatinine (SCr) 69.3 +/- 12 and 71.1 +/- 13 micromol/l; and 24-h urine protein 0.12 +/- 0.05 and 0.09 +/- 0.03 g (all P = NS) while at 12 months, 24-h SBP were 111.4 +/- 11 and 114.3 +/- 8 mmHg (P = 0.384), SCr 97.9 +/- 16 and 97.7 +/- 21 micromol/l (P = 0.810); and 24-h urine protein 0.139 +/- 0.09 and 0.111 +/- 0.07 g/d (P = 0.360) respectively. The 24-h SBP was significantly lower in the dippers at 12 months as compared with predonation (P = 0.036). OGTT and lipid profiles remained normal in both groups. Predonation nocturnal nondipping does not carry adverse postdonation consequences over 12 months.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.545
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.010
GPT teacher head0.238
Teacher spread0.228 · 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

Citations13
Published2010
Admission routes1
Has abstractyes

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