Unique Toxicities of Novel Myeloma Therapies: Focus on Belantamab Mafodotin, Talquetamab, and Selinexor
Bibliographic record
Abstract
Recent survival improvements in patients with multiple myeloma are attributable largely to the introduction of three main drug classes, immunomodulatory drugs (IMIDs), proteasome inhibitors, and anti-CD38 monoclonal antibodies. However, the majority of patients will inevitably develop resistance to all three drug classes, and survival in this setting has been historically poor. Several novel therapeutic classes exploiting new mechanisms of action (e.g., chimeric antigen receptor (CAR)-T cell therapy, bispecific antibodies, antibody-drug conjugates (ADC), and selective inhibitors of nuclear export) have shown high levels of activity in relapsed myeloma and promise to transform the treatment landscape. However, these therapies have been associated with distinct toxicity profiles, with adverse effects that are uncommonly observed with conventional antimyeloma therapies. Given improvements in long-term disease control and survival with current therapies, treatment-related toxicity represents an increasingly important health burden in patients with myeloma, and the development of effective toxicity management strategies is required to minimize complications and ensure preserved quality of life.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".