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Record W75498373

Effect of chronic kidney disease on red blood cell rheology.

2006· article· en· W75498373 on OpenAlexaff
K. Scott Brimble, Andrew McFarlane, Nancy Winegard, Mark Crowther, David Churchill

Bibliographic record

VenuePubMed · 2006
Typearticle
Languageen
FieldMedicine
TopicBlood properties and coagulation
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineBlood viscosityErythropoietinHemodialysisKidney diseaseInternal medicineAnemiaRed blood cellRenal functionKidneyEndocrinologyGastroenterologyHemorheologyUrology
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: Anemia of renal failure is primarily a problem of decreased RBC production due to erythropoietin deficiency. RBC survival is also reduced, perhaps due to decreased RBC deformability. This study measured blood viscosity over a range of shear rates in erythropoietin-treated patients on hemodialysis (HD), and compared the findings to matched patients with chronic renal insufficiency (CRI) and healthy controls. METHODS: Four groups (control, CRI, non-diabetic HD, and diabetic HD) of 9 matched subjects were recruited. Blood viscosity was measured using a cone-plate viscometer over a variety of shear rates (11 to 225 s(-1)). RESULTS: Control subjects had lower viscosity values throughout all shear rates when compared to the 3 renal disease groups (P value=0.039). A trend was observed to higher levels of renal function being associated with decreased blood viscosity in patients with CRI. CONCLUSIONS: Patients with kidney disease have increased blood viscosity at all shear rates. This may be related to changes in RBC shape and decreased deformability in patients with kidney disease, independent of HD- or DM-status. This may have implications for strategies to treat anemia 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 score0.327
Threshold uncertainty score0.266

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)0.0000.000

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 head0.006
GPT teacher head0.194
Teacher spread0.188 · 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

Citations38
Published2006
Admission routes1
Has abstractyes

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