Real-world outcomes with novel therapies in relapsed/refractory diffuse large B-cell lymphoma
Bibliographic record
Abstract
This study used COTA de-identified data (2010-2021) of patients in the US to explore outcomes of novel therapies in relapsed/refractory (R/R) diffuse large B-cell lymphoma (DLBCL) in real-world settings. Demographics, clinical characteristics, and clinical outcomes of patients with R/R DLBCL who received novel treatments including chimeric antigen receptor T-cell (CAR T) therapy and tafasitamab- or polatuzumab-based therapies were evaluated. Overall, 175 patients with R/R DLBCL were analyzed; 73, 69, and 27 received CAR T therapy, polatuzumab-based regimens, and tafasitamab-based regimens, respectively. In patients who had ≥1 prior lines of therapy (i.e. starting second-line or later therapy; 2 L+), CAR T, polatuzumab-based regimens, and tafasitamab-based regimens achieved a median overall survival of 26.5, 7.8, and 6.3 months, respectively. Outcomes were particularly poor for patients with relapse following CAR T, indicating that polatuzumab- and tafasitamab-based regimens in 2 L + R/R DLBCL have suboptimal outcomes in the real world. Additional treatment options are needed.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".