Genomic stratification of multiple myeloma treated with novel agents
Bibliographic record
Abstract
Allan Jianga, Donna Reecea & Hong Chang*aa Department of Laboratory Hematology and Medical Oncology, University Health Network, Toronto, CanadaCorrespondence: Hong Chang, MD, PhD, FRCPC, Department of Laboratory Hematology, University Health Network, 200 Elizabeth St, 11E-413, Toronto, Ontario, Canada, M5G 2C4. Tel: 416-340-4858. Fax: 416-340-5543. E-mail: Hong.Chang@uhn.on.caAbstractCytogenetic testing is now routinely performed for the prognostic work-up of multiple myeloma (MM). The abnormalities del(17p), t(4;14) and del(13q) have been established as predictors of poor outcome in patients with MM treated with conventional chemotherapy or stem cell transplant; chromosome 1q gains and 1p losses have also been identified as novel prognostic factors. In recent years, bortezomib and lenalidomide have emerged as effective treatments for both relapsed/refractory and newly diagnosed MM. However, the effect of cytogenetic abnormalities is unclear among patients with MM treated with these novel agents. Here we review recent studies that analyze the impact of specific genomic aberrations on the outcome of MM treated with bortezomib and/or lenalidomide.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".