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Record W2588037807 · doi:10.1093/ndt/gfw163.19

SP238BTP ASSAYS-A COMPARISON BETWEEN NEPHELOMETRIC AND ELISA METHODOLOGIES

2016· article· en· W2588037807 on OpenAlexaff
Debarati Chakraborty, Ayub Akbari, Greg Knoll, Christine A. White

Bibliographic record

VenueNephrology Dialysis Transplantation · 2016
Typearticle
Languageen
FieldImmunology and Microbiology
TopicBiosimilars and Bioanalytical Methods
Canadian institutionsUniversity of OttawaQueen's University
Fundersnot available
KeywordsMedicine

Abstract

fetched live from OpenAlex

Introduction and Aims: Beta-trace protein (BTP) is a heterogeneous monomeric glycoprotein and an emerging novel marker of GFR and cardiovascular health. Several equations now exist to convert its serum concentration into an estimate of GFR. There are only two commercially available assays to quantitate BTP. Cayman Chemicals provides an ELISA assay using monoclonal murine antibodies. Siemens offers a nephelometric assay utilizing polyclonal rabbit antibodies against human urinary BTP. Higher order reference materials and methods do not exist. Differences between the two assays have never been examined. The aim of this study was to determine the difference in BTP concentrations using the 2 assays and examine the impact on GFR estimation. Methods: Residual frozen serum from 105 subjects enrolled in a prospective study examining the impact of hepatic dysfunction on BTP concentrations was split and refrozen. BTP was measured in the split samples using the two assays. For each sample and assay, GFR was estimated using the new EPI BTP equation (Inker et al, AJKD 2016): BTP GFR=55 * BTP -0.695 * 0.998 age * 0.899 if female. Differences between paired BTP values were calculated and compared using paired student t-tests. The percentage of samples with paired values within 10%, 20% and 30% of each other was calculated. A similar analysis was performed using the EPI BTP GFR for the whole cohort and after stratification by the median of the average of the EPI BTP GFR of the two assays.

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)0.001
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 score0.581
Threshold uncertainty score0.621

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.060
GPT teacher head0.333
Teacher spread0.273 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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