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Role of dialysis technology in the removal of uremic toxins

2011· review· en· W1910846860 on OpenAlexvenueno aff
Andrew Davenport

Bibliographic record

VenueHemodialysis International · 2011
Typereview
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsnot available
Fundersnot available
KeywordsHemodialysisMedicineDialysisBeta-2 microglobulinUreaUremic toxinsIntensive care medicinePhosphateKidney diseaseChromatographyUrologyInternal medicineChemistryBiochemistry

Abstract

fetched live from OpenAlex

Traditionally, the amount of hemodialysis prescribed for a patient has been based on urea clearance, as urea is not only retained in patients with chronic kidney disease, but also readily measurable, by reliable and inexpensive assays. More recently, other retained solutes, phosphate, β2 microglobulin, and latterly p-cresol have been reported to be associated with increased risk of mortality in hemodialysis patients. As such, developments in dialysis practice that would result in greater clearance of water-soluble middle-sized toxins and also protein-bound and/or organic solutes are being studied. Although session time is a key factor, switching from low flux to dialyzers with larger pores, the addition of convective transport with hemodiafiltration can help increase phosphate and β2 microglobulin clearances. Adsorption techniques can increase the clearance of organic and protein bound toxins either directly or indirectly by regenerating dialysate and ultrafiltrates.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.028
GPT teacher head0.308
Teacher spread0.280 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations19
Published2011
Admission routes1
Has abstractyes

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