IgMκ and IgMλ Measurements for the Assessment of Patients with Waldenström's Macroglobulinaemia
Bibliographic record
Abstract
PURPOSE: Accurate quantification of monoclonal IgM immunoglobulins is essential for response assessment in patients with Waldenström's macroglobulinaemia (WM). The propensity of IgM to form multimers in serum makes sample evaluation by current laboratory methods particularly challenging. EXPERIMENTAL DESIGN: We assessed the precision and linearity of IgMκ and IgMλ heavy/light chain (HLC, Hevylite) assays, and established reference intervals using 120 normal donor sera. We compared the quantitative performance of HLC assays with serum protein electrophoresis (SPE) and total IgM nephelometry for 78 diagnostic samples and follow-up samples from 25 patients with WM. Comparisons were made between the three methods for diagnostic sensitivity and response assessment. RESULTS: = 0.49). Analysis of 120 normal donor sera produced the following normal ranges: IgMκ: 0.29-1.82 g/L; IgMλ: 0.17-0.94 g/L; IgMκ/IgMλ ratio: 0.96-2.30. Using these ranges, IgM HLC ratios were abnormal in all WM presentation sera tested, including 15 with non-quantifiable SPE. Despite discordance in quantitation, responses assigned with HLC assays showed excellent agreement to those based on international guidelines using SPE or total IgM; although abnormal HLC ratios indicated residual disease in some patients with negative electrophoresis results. CONCLUSIONS: Nephelometric assessment of IgMκ and IgMλ HLC pairs offers a quantitative alternative to traditional laboratory techniques for the measurement of monoclonal IgM and may aid in the management of WM. Clin Cancer Res; 22(20); 5152-8. ©2016 AACR.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".