An international multi-center serum protein electrophoresis accuracy and M-protein isotyping study. Part II: limit of detection and follow-up of patients with small M-proteins
Bibliographic record
Abstract
Background Electrophoretic methods to detect, characterize and quantify M-proteins play an important role in the management of patients with monoclonal gammopathies (MGs). Significant uncertainty in the quantification and limit of detection (LOD) is documented when M-proteins are <10 g/L. Using spiked sera, we aimed to assess the variability in intact M-protein quantification and LOD across 16 laboratories. Methods Sera with normal, hypo- or hyper-gammaglobulinemia were spiked with daratumumab or elotuzumab, with concentrations from 0.125 to 10 g/L (n = 62) along with a beta-migrating sample (n = 9). Laboratories blindly analyzed samples according to their serum protein electrophoresis (SPEP)/isotyping standard operating procedures. LOD and intra-laboratory percent coefficient of variation (%CV) were calculated and further specified with regard to the method (gel/capillary electrophoresis [CZE]), gating strategy (perpendicular drop [PD]/tangent skimming [TS]), isotyping (immunofixation/immunosubtraction [ISUB]) and manufacturer (Helena/Sebia). Results All M-proteins ≥1 g/L were detected by SPEP. With isotyping the LOD was moderately more sensitive than with SPEP. The intensity of polyclonal background had the biggest negative impact on LOD. Independent of the method used, the intra-laboratory imprecision of M-protein quantification was small (mean CV = 5.0%). Low M-protein concentration and high polyclonal background had the strongest negative impact on intra-laboratory precision. All laboratories were able to follow trend of M-protein concentrations down to 1 g/L. Conclusions In this study, we describe a large variation in the reported LOD for both SPEP and isotyping; overall LOD is most affected by the polyclonal immunoglobulin background. Satisfactory intra-laboratory precision was demonstrated. This indicates that the quantification of small M-proteins to monitor patients over time is appropriate, when subsequent testing is performed within the same laboratory.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".