Consensus Guidelines on the Diagnosis of Multiple Myeloma and Related Disorders: Recommendations of the Myeloma Canada Research Network Consensus Guideline Consortium
Bibliographic record
Abstract
Multiple myeloma (MM) is a plasma cell (PC) malignancy of terminally differentiated B lymphocytes that is typically associated with the secretion of partial and/or complete monoclonal immunoglobulins and a constellation of particular symptoms and signs. MM is a treatable condition, and timely diagnosis is essential to limit or avoid irreversible target-organ damage and to prolong survival. The Myeloma Canada Research Network Consensus Guideline Consortium (MCRN-CGC) proposes national consensus recommendations for the diagnosis of MM and associated PC neoplasms. The focus is on widely available tests but also highlights recent advancements that are important to include in the diagnostic paradigm. By clarifying and updating the required laboratory, radiographic, and bone marrow investigations, the MCRN-CGC hopes to address the needs of Canadian physicians and people living with MM across the country through accurate and timely diagnosis of MM, as well as appropriate initial stratification to improve treatment selection and outcomes. The MCRN-CGC will periodically review the recommendations herein and update as necessary. Recommendations on the therapeutic approaches and associated monitoring of MM will follow.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.011 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.010 | 0.003 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".