Guidelines for the testing and reporting of cytogenetic results for risk stratification of multiple myeloma: a report of the Cancer Genomics Consortium Plasma Cell Neoplasm Working Group
Bibliographic record
Abstract
Fluorescence in situ hybridization (FISH) remains the gold-standard clinical assay to detect genetic abnormalities in multiple myeloma (MM). However, FISH panel design, use of conventional chromosome banding analysis and reporting practices have been reported to vary among laboratories. Therefore, standardization in FISH testing and reporting practices is needed to improve report clarity and avoid misinterpretation. The recommendations in this paper represent a consensus of our Cancer Genomics Consortium Plasma Cell Neoplasm Working Group, comprising a joint panel of cytogenetic laboratory directors and clinical investigators with expertise in the diagnosis, risk stratification, and treatment of multiple myeloma. Prior to developing these consensus recommendations, we performed a full literature review and conducted a survey of 102 oncologists to assess current variations and challenges in MM cytogenetic/FISH testing and reporting. Our guidelines establish best practices for the optimization of FISH panel selection, and recommendations for standardized reporting of cytogenetic results to align with the 2025 International Myeloma Society (IMS)/International Myeloma Working Group (IMWG) Updated Risk Stratification.
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 | 0.031 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".