Comparison of Two Different Integration Methods for Quantifying Monoclonal Proteins on Agarose Gel and Capillary Zone Electrophoresis Instruments
Bibliographic record
Abstract
Quantifying M-proteins is an important part of diagnosing and monitoring patients with monoclonal gammopathies. Historically, laboratories use one of two methods to accomplish this. The splice method utilizes a perpendicular drop on each side of the M-protein on the electrophoretogram. In contrast, the skim method applies a tangent skimming line connecting the points above the polyclonal background. In this study, we compared the bias between these two methods across two different instruments (Helena SPIFE 3000 and Sebia Capillarys 3) in 118 patients. First, we compared the splice technique on both instruments and observed a significant average bias of 58.3% (slope = 1.437, y-intercept = 0.76, and r = 0.9682). We next compared the splice technique on the SPIFE 3000 to the skim technique on the Capillarys 3 and observed an average bias of only −2.10% (slope = 1.363, y-intercept = −1.98, and r = 0.9716), although there was significant scatter along the line of best fit. Lastly, we compared splice vs. skim on the Capillarys 3 and observed an average bias of −38.2% (slope = 0.947, y-intercept = −2.65, and r = 0.9686). Based on these results, care should be taken when switching instruments or integration techniques to ensure consistent monitoring of patients.
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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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".