Genetics and Cytogenetics of Multiple Myeloma
Bibliographic record
Abstract
Much has been learned regarding the biology and clinical implications of genetic abnormalities in multiple myeloma. Because of recent advances in the field, an International Workshop was held in Paris in february of 2003. This summary describes the consensus recommendations arising from that meeting with special emphasis on novel genetic observations. For instance, it is increasingly clear that translocations involving the immunoglobin heavy-chain locus are important for the pathogenesis of one-half of patients. As a corollary, it also clear that the remaining patients, lacking IgH translocations, have hyperdiploidy as the hallmark of their disease. Several important genetic markers are associated with a shortened survival such as chromosome 13 monosomy, hypodiploidy, and others. The events leading the transformation of the monoclonal gammopathy of undetermined significance (MGUS) to myeloma are still unclear. One of the few differential genetic lesions between myeloma and MGUS is the presence of ras mutations in the latter. Gene expression platforms are capable of detecting many of the genetic aberrations found in the clonal cells of myeloma. Areas in need of further study were identified. The study of the genetic aberrations will likely form the platform for targeted therapy for the disease.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".