Bibliographic record
Abstract
PURPOSE OF REVIEW: The purpose of this review is to present updates in the field of bispecific antibodies focusing on those agents that have been recently approved for multiple myeloma, follicular lymphoma and diffuse large B cell lymphoma. RECENT FINDINGS: Teclistamab, the β-cell maturation antigen -targeted bispecific antibody has shown efficacy and tolerability in the fourth line setting for multiple myeloma. Mosunetuzumab, the CD20-targeted bispecific antibody has shown excellent response rates and durability in third line and beyond follicular lymphoma. Epcoritamab and glofitamab have both shown excellent response rates in heavily pretreated patients with diffuse large B cell lymphoma including those with prior chimeric antigen receptor T cell therapy. The toxicity is significant but manageable for both agents. Epcoritamab is approved by the FDA in the United States, while glofitamab is approved for use in Canada for patients with diffuse large B cell lymphoma refractory to 2 or more prior lines of therapy. SUMMARY: Bispecific antibodies represent a novel therapeutic resource that is poised to dramatically change the treatment landscape of many hematologic malignancies, but so far, initial successes include multiple myeloma, follicular lymphoma, and diffuse large B cell lymphoma, where several agents have been recently approved.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".