Diagnostics in Waldenström’s macroglobulinemia: a consensus statement of the European Consortium for Waldenström’s Macroglobulinemia
Bibliographic record
Abstract
The diagnosis of Waldenström's macroglobulinemia (WM), an IgM-associated lymphoplasmacytic lymphoma, can be challenging due to the different forms of disease presentation. Furthermore, in recent years, WM has witnessed remarkable progress on the diagnostic front, as well as a deeper understanding of the disease biology, which has affected clinical practice. This, together with the increasing variety of tools and techniques available, makes it necessary to have a practical guidance for clinicians to perform the initial evaluation of patients with WM. In this paper, we present the consensus recommendations and laboratory requirements for the diagnosis of WM developed by the European Consortium of Waldenström's Macroglobulinemia (ECWM), for both clinical practice as well as the research/academical setting. We provide the procedures for multiparametric flow cytometry, fluorescence in situ hybridization and molecular tests, and with this offer guidance for a standardized diagnostic work-up and methodological workflow of patients with IgM monoclonal gammopathy of uncertain significance, asymptomatic and symptomatic WM.
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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 | 0.088 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".