The Tao of myeloma
Bibliographic record
Abstract
Multiple myeloma is a plasma cell malignancy in which significant advances have been observed during the last 15 years. Our understanding of the disease has been advanced through its molecular characterization. We have also seen improvements in patient care with the development of 2 new classes of active agents, proteasome inhibitors and immunomodulatory drugs (IMiDs), resulting in a significant improvement in overall survival of myeloma patients such that it can now be debated as to whether some subsets of myeloma patients can be cured. However, the advances in our understanding of myeloma biology occurred in parallel with advances in treatment as opposed to being directly informed by the research. Moreover, the molecular characterization of malignant plasma cells would not have predicted the effectiveness of these novel therapies.We hypothesize that proteasome inhibitors and IMiDs are highly active because malignant plasma cells are constrained by many of the characteristics of their normal counterparts and these novel therapies target both normal plasma cell biology and the cancer biology of myeloma. Thus, a better understanding of normal plasma cell biology will likely yield as many actionable targets as mapping the genomic landscape of this 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".