Brentuximab Vedotin Retreatment in Patients with Relapsed or Refractory Classical Hodgkin Lymphoma or Peripheral T-Cell Lymphoma: A Retrospective United States Claims Analysis
Bibliographic record
Abstract
Brentuximab vedotin (BV) monotherapy (BV-M) and combination (BV-C) therapies are safe and effective for classical Hodgkin lymphoma (cHL) and CD30-expressing peripheral T-cell lymphomas (PTCLs). Although the sample sizes have been small (12–29 patients), in clinical studies, response rates of 53–88% have been reported for BV retreatment in patients with an initial BV response. We evaluated the real-world characteristics and treatment patterns of cHL/PTCL patients who received BV and were retreated in the United States. Symphony Health Patient Claims (11/2013–1/2022) were retrospectively analyzed to identify cHL/PTCL patients treated with BV and retreated with BV-M, BV-C, or non-BV therapy. Patient characteristics were described by retreatment, and predictors of BV-M retreatment were identified. Among the cHL and PTCL patients treated with BV (n = 6442 and 2472, respectively), 13% and 12%, respectively, were retreated with BV; the median times from initial BV to BV-M retreatment were 5 and 7 months, respectively; and the numbers of BV-M retreatment doses were 4 and 5, respectively. Among cHL patients, the predictors of BV-M retreatment were age (18–39 vs. ≥60 years), sex (women vs. men), and previous stem cell transplantation (yes vs. no). Among PTCL patients, the only predictor of BV-M retreatment was systemic anaplastic large-cell lymphoma subtype (yes vs. no). Real-world data support clinical study results suggesting earlier BV treatment be considered, as BV retreatment may be an option.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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".