Use of a solid-phase extraction with radioimmunoassay to identify the proportional bias of clinical B-type natriuretic peptide immunoassay: the impact of plasma matrix and antibody multispecificity
Bibliographic record
Abstract
BACKGROUND: Circulating immunoreactive B-type natriuretic peptide-32 (ir-BNP-32) has diagnostic and prognostic values in heart failure. We compared, in parallel, a point-of-care (POC) test (Triage((R)) BNP Test) of whole plasma and radioimmunoassay (RIA) of solid-phase extracted (SPE) plasma (SPE/RIA) utilizing a novel copolymer column, in the measurement of patient ir-BNP-32 concentrations. METHODS: Approximately 0.25 mL thawed plasma was transferred to a BNP test device and inserted in a Triage Meter Plus, which gave ir-BNP-32 concentration in pg/mL. Concurrently, for the SPE/RIA measurement, 1.0 mL plasma was acidified and extracted with an OASIS column; eluate dried, reconstituted and quantified by RIA. RESULTS: Inter-day coefficient of variation for both methods were <15%. Plasma SPE recovery was 75.2%. POC correlated with recovery corrected SPE/RIA for ir-BNP-32, r=0.843 (p<0.0001) and the Passing-Bablok model was POC ir-BNP-32=1.43x (recovery corrected SPE/RIA ir-BNP-32)+9.75 ng/L (n=81). A proportional bias was also evident from the Bland-Altman plot, r=0.716 (p<0.0001). CONCLUSIONS: A proportional bias is responsible for plasma ir-BNP-32 concentration differences between whole plasma POC test and recovery corrected SPE/RIA measurements. Ir-BNP-32 assays are influenced by plasma matrix and antibody multispecificity. Consequently, consistent analytical accuracy between immunoassays is necessary to attain a single ir-BNP-32 concentration threshold for diagnosis. Clin Chem Lab Med 2007;45:1353-9.
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 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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".