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Record W1976579265 · doi:10.4236/ojneph.2013.33029

Study of Dialyzer Membrane (Polyflux 210H) and Effects of Different Parameters on Dialysis Performance

2013· article· en· W1976579265 on OpenAlexaff
Md Shihamul Islam, Jerzy A. Szpunar

Bibliographic record

VenueOpen Journal of Nephrology · 2013
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMembraneDialysisComplement systemMembrane structureUreaChemistryAlbuminBlood flowChromatographyBeta-2 microglobulinDialysis tubingBiomedical engineeringBiophysicsMaterials scienceBiochemistryMedicineInternal medicineImmunologyBiology

Abstract

fetched live from OpenAlex

Problems frequently encountered in kidney malfunction include abnormal fluid levels in the body, increased acid levels, abnormal levels of Urea, Glucose, Endothelin, β2-Microglobulin and Complement Factor D. Parameters characterizing the structure of dialyzers are very important because they decide overall clearance of toxin molecules and at the same time should allow retaining useful molecules in the blood. In this paper, a cross sectional image of the dialyzer membrane with details of the porosity is presented. A multilayered membrane model with different porosity for each layer, describes the actual structure of Polyflux 210H membrane. This model is developed using Finite Element Software—COMSOL Multiphysics 4.3. A blood flow with substances like—Urea, Glucose, Endothelin, β2-Microglobulin, Complement Factor D and Albumin is introduced. For a certain blood flow rate, the toxins diffuse through the membrane and on the other side of the membrane a dialysate flow removes the toxins. Here, different parameters, such as flow rate of blood and dialysate, length and radius of the fiber are changed to simulate how these changes affect toxin clearance and the removal of useful molecules.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.638
Threshold uncertainty score0.352

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.015
GPT teacher head0.262
Teacher spread0.248 · 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

Citations20
Published2013
Admission routes1
Has abstractyes

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