Prognostic and Therapeutic Significance of Myeloma Genetics and Gene Expression Profiling
Bibliographic record
Abstract
Molecular diagnostic tools and novel therapeutics now offer the potential for accurate prognostic and personalized treatment road maps for patients with multiple myeloma (MM). We will review the evidence and provide specific recommendations for routine clinical molecular genetic testing and use of such information to guide therapeutic decision making. In particular, the negative prognostic impact of specific IgH translocations such as the t(4;14), t(14;16), chromosome 13 deletion by conventional cytogenetics and loss of 17p13 by interphase fluorescence in situ hybridization are now established. Preliminary gene expression profiling studies have also demonstrated that individual genes (CSK1-B) or groups of genes can define prognosis with greater accuracy than conventional genetic markers and can provide pharmacogenomic and biologic insight into the pathophysiology, therapeutics, and future targets of myeloma. Importantly, we recommend that all clinical trials now adopt routine genetic testing and risk stratification.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".