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Record W2898300033 · doi:10.1111/hdi.12685

Removal capacity of different high‐flux dialyzers during postdilution online hemodiafiltration

2018· article· en· W2898300033 on OpenAlexvenueno aff
Alba Santos García, Nicolás Macías Carmona, Almudena Vega Martínez, Soraya Abad Estébanez, Tania Linares Grávalos, Inés Aragoncillo Sauco, Úrsula Verdalles Guzmán, Nayara Panizo, Leónidas Cruzado Vega, Juan Manuel López Gómez

Bibliographic record

VenueHemodialysis International · 2018
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineFlux (metallurgy)HemodialysisInternal medicineMetallurgyMaterials science

Abstract

fetched live from OpenAlex

INTRODUCTION: The aim of this study is to compare molecule removal and albumin leakage in postdilution online hemodiafiltration with different high-flux dialyzers. METHODS: We studied seven high-flux dialyzers (Polyflux 210H®, Evodial 2.2®, FxCordiax1000®, Elisio21H®, TS-2.1SL®, XevontaHi20®, VitaPES 210-HF®) in 6 patients. The reduction ratio (RR) of small- and middle-sized molecules was calculated. Dialysate samples were collected to estimate the albumin leakage. FINDINGS: Global differences between dialyzers were observed in the RR of ß2 microglobulin (P =0.003) and prolactin (P =0.013). The mean loss of albumin in the dialysate per session varied between 114 ± 67 mg (with Evodial 2.2) and 2621 ± 1363 mg per session (with XevontaHi20). We found global differences between dialyzers in total albumin loss (P = 0.05). DISCUSSION: We demonstrated that the performance of high-flux dialyzers was different among the types and that not all high-flux dialyzers should be considered equal.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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

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 head0.017
GPT teacher head0.261
Teacher spread0.243 · 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

Citations9
Published2018
Admission routes1
Has abstractyes

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