An international multi-center serum protein electrophoresis accuracy and M-protein isotyping study. Part I: factors impacting limit of quantitation of serum protein electrophoresis
Bibliographic record
Abstract
Background Serum protein electrophoresis (SPEP) is used to quantify the serum monoclonal component or M-protein, for diagnosis and monitoring of monoclonal gammopathies. Significant imprecision and inaccuracy pose challenges in reporting small M-proteins. Using therapeutic monoclonal antibody-spiked sera and a pooled beta-migrating M-protein, we aimed to assess SPEP limitations and variability across 16 laboratories in three continents. Methods Sera with normal, hypo- or hypergammaglobulinemia were spiked with daratumumab, Dara (cathodal migrating), or elotuzumab, Elo (central-gamma migrating), with concentrations from 0.125 to 10 g/L (n = 62) along with a beta-migrating sample (n = 9). Provided with total protein (reverse biuret, Siemens), laboratories blindly analyzed samples according to their SPEP and immunofixation (IFE) or immunosubtraction (ISUB) standard operating procedures. Sixteen laboratories reported the perpendicular drop (PD) method of gating the M-protein, while 10 used tangent skimming (TS). A mean percent recovery range of 80%-120% was set as acceptable. The inter-laboratory %CV was calculated. Results Gamma globulin background, migration pattern and concentration all affect the precision and accuracy of quantifying M-proteins by SPEP. As the background increases, imprecision increases and accuracy decreases leading to overestimation of M-protein quantitation especially evident in hypergamma samples, and more prominent with PD. Cathodal migrating M-proteins were associated with less imprecision and higher accuracy compared to central-gamma migrating M-proteins, which is attributed to the increased gamma background contribution in M-proteins migrating in the middle of the gamma fraction. There is greater imprecision and loss of accuracy at lower M-protein concentrations. Conclusions This study suggests that quantifying exceedingly low concentrations of M-proteins, although possible, may not yield adequate accuracy and precision between laboratories.
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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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".