MétaCan
Menu
← Back to cohort
Record W4309358048 · doi:10.1002/9781119105954.ch45

Small Solute Clearance in Peritoneal Dialysis

2022· other· en· W4309358048 on OpenAlexaff
Kannaiyan S Rabindranath, Sharon J. Nessim, Joanne M. Bargman

Bibliographic record

Venuenot available
Typeother
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsMcGill UniversityUniversity of TorontoJewish General Hospital
Fundersnot available
KeywordsPeritoneal dialysisMesotheliumPeritoneumPeritonitisSerous fluidMedicineDialysisPeritoneal equilibration testUrologySemipermeable membraneChemistryPharmacologyGastroenterologyInternal medicineSurgeryMembraneBiochemistryContinuous ambulatory peritoneal dialysis

Abstract

fetched live from OpenAlex

The ideal method of assessing the adequacy of peritoneal dialysis (PD) has yet to be determined. While measures of solute clearance have traditionally focused on small solutes such as urea and creatinine, little attention has been paid to clearance of larger molecular weight uremic toxins, the so-called “middle molecules” and protein-bound uremic toxins which may have important pathophysiological impacts on mortality and morbidity. The peritoneum is a serous semipermeable membrane composed of a thin layer of connective tissue covered by a mesothelial cell monolayer. The peritoneal equilibration test is the most widely used test to characterize the rate of solute and water transfer in patients undergoing PD. The routine measurement of small solute clearance serves to screen patients for evidence of underdialysis. Aminoglycosides are frequently used to treat peritonitis in PD patients because of their effectiveness as bactericidal agents and the ease of intraperitoneal administration.

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)0.004
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: Other · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
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 head0.018
GPT teacher head0.245
Teacher spread0.227 · 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
GenreOther

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

Citations2
Published2022
Admission routes1
Has abstractyes

Explore more

Same topicDialysis and Renal Disease Management→French-language works237,207→