Second- and third-line treatment strategies in multiple myeloma: a referral-center experience
Bibliographic record
Abstract
The treatment landscape for relapsed multiple myeloma (MM) has increased. In this study, we aimed to characterize 2nd (n = 1439) and 3rd (n = 1104) line regimens and compare the results between subgroups based on the year of treatment initiation (2nd line: 2003-2008, 2009-2015, 2016-2021; 3rd line: 2004-2009, 2010-2015, and 2016-2021). In both the second- and third- lines, we observed increasing use of novel agents (from 78 to 95% and from 77 to 95%, respectively) and triplet regimens (from 15 to 69% and from 21 to 71%, respectively). The most frequently used regimens in the last studied periods included lenalidomide-dexamethasone (RD; 14%), carfilzomib-RD (12%), and daratumumab-RD (10%) for the second-line, and daratumumab-pomalidomide-dexamethasone (11%) and daratumumab-RD (10%) for the third-line. The median time to the next treatment from second-line therapy has improved from 10.4 months (95% CI: 8.4-12.4) to 16.6 months (95% CI: 13.3-20.3; p < 0.001). The median overall survival from the first relapse increased from 30.9 months (95% CI: 26.8-183.0) to 65.8 months (95% CI: 50.7-72.8; p < 0.001). Over the last two decades, more patients were treated with newer agents and triplets for relapsed MM. The landscape of regimens has become more diverse, and survival after the first relapse is continually improving.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".