The Association between Patient Characteristics and the Efficacy and Safety of Selinexor in Diffuse Large B-Cell Lymphoma in the SADAL Study
Bibliographic record
Abstract
Selinexor, an oral selective inhibitor of nuclear export, was evaluated in the Phase 2b SADAL study in patients with diffuse large B-cell lymphoma (DLBCL) who previously received two to five prior systemic regimens. In post hoc analyses, we analyzed several categories of patient characteristics (age, renal function, DLBCL subtype, absolute lymphocyte count, transplant status, number of prior lines of therapy, refractory status, Ann Arbor disease stage, and lactate dehydrogenase) at baseline, i.e., during screening procedures, to determine their potential contributions to the efficacy (overall response rate [ORR], duration of response [DOR], overall survival [OS]) and tolerability of selinexor. Across most categories of characteristics, no significant difference was observed in ORR or DOR. OS was significantly longer for patients < 65 vs. ≥ 65 years, and for those with lymphocyte counts ≥ 1000/µL vs. < 1000/µL or lactate dehydrogenase ≤ ULN vs. > ULN. The most common adverse events (AEs) across the characteristics were thrombocytopenia and nausea, and similar rates of grade 3 AEs and serious AEs were observed. With its oral administration, novel mechanism of action, and consistency in responses in heavily pretreated patients, selinexor may help to address an important unmet clinical need in the treatment of DLBCL.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".