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Rate of creatinine equilibration in whole blood

2009· article· en· W1974507163 on OpenAlexvenueno aff
Daniel Schneditz, Yichien YANG, Georgios Christopoulos, Josef Kellner

Bibliographic record

VenueHemodialysis International · 2009
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsnot available
Fundersnot available
KeywordsCreatinineChemistryChromatographyDiffusionBlood flowMedicineInternal medicineThermodynamicsBiochemistry

Abstract

fetched live from OpenAlex

The classic assumption that a large fraction of blood creatinine remains sequestered within erythrocytes when blood is dialyzed has been challenged by recent observations where approximately 60% of erythrocyte water appeared accessible to diffusive creatinine transport during a dialyzer transit. This discrepancy provided the motivation to revisit and reanalyze the equilibration of creatinine across the erythrocyte membrane in a series of in vitro studies with normal human blood under erythrocyte loading and unloading conditions at 37 degrees C. The time course of plasma creatinine concentrations measured by a kinetic picric acid assay was analyzed using a 2-compartment model. In 7 experiments, the equilibration constant was 0.052 +/- 0.013/min, corresponding to a mean half-life of 13.8 +/- 2.8 minutes, and comparable for erythrocyte loading and unloading. With these values and with mean dialyzer transit times in the range of 20 seconds the fraction of erythrocyte water accessible to diffusive clearance is in the range of 2%. These results are comparable to what has been measured with radiolabeled markers almost half a century ago. Therefore, when dialyzer outlet concentrations are sampled without equilibration the effective diffusion volume flow rate for creatinine is close to plasma water flow and does not include sizeable fractions of erythrocyte water flow.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.012
GPT teacher head0.274
Teacher spread0.261 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations24
Published2009
Admission routes1
Has abstractyes

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