SP238BTP ASSAYS-A COMPARISON BETWEEN NEPHELOMETRIC AND ELISA METHODOLOGIES
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".