Real-World Evidence Evaluating Teclistamab in Patients with Relapsed/Refractory Multiple Myeloma: A Systematic Literature Review
Bibliographic record
Abstract
Background: Teclistamab (TEC) is the first B-cell maturation antigen-directed bispecific antibody approved in 2022 by the European Medicines Agency and Food and Drug Administration for triple-class exposed relapsed/refractory multiple myeloma (RRMM). Objectives: As TEC is increasingly used in real-world (RW) settings, this study seeks to gather existing RW evidence on effectiveness, safety, healthcare resource utilization, and clinical practices associated with TEC. Methods: A systematic literature review was performed to identify RW observational studies of TEC-treated adults with RRMM from 2023 to June 2024. Results: Sixty-one records representing 41 unique studies were included; sample sizes ranged from 8 to 572 patients. Where reported, median follow-up ranged from 2.3 to 33.6 months, and >65% of the patients would have been ineligible for the pivotal trial of TEC (MajesTEC-1) in all but one study. In eight studies with ≥50 patients and ≥3 months follow-up, overall response rates were 59–66% and cytokine release syndrome (CRS) rates were 18–64%. Tocilizumab use for CRS management was reported in 14 studies, with two indicating CRS rates of 13% and 26% when used prophylactically. Survival and infection outcomes showed wide variability due to short follow-up in most studies. Conclusions: Overall, early RW effectiveness and safety outcomes of TEC were comparable to findings from MajesTEC-1.
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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 | 0.010 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".