Brentuximab vedotin after autologous transplantation in pediatric patients with relapsed/refractory Hodgkin lymphoma
Bibliographic record
Abstract
Outcomes for children and adolescents with relapsed and refractory Hodgkin lymphoma (HL) are poor, with ∼50% of patients experiencing a subsequent relapse. The anti-CD30 antibody-drug conjugate brentuximab vedotin improved progression-free survival (PFS) when used as consolidation after autologous stem cell transplantation (ASCT) in adults with high-risk relapsed/refractory HL. Data on brentuximab vedotin as consolidative therapy after ASCT in pediatric patients with HL are extremely limited, with data of only 11 patients reported in the literature. We performed a retrospective analysis of 67 pediatric patients who received brentuximab vedotin as consolidation therapy after ASCT for the treatment of relapsed/refractory HL to describe the experience of this regimen in the pediatric population. This is the largest cohort reported to date. We found that brentuximab vedotin was well tolerated with a safety profile similar to that of adult patients. With a median follow-up of 37 months, the 3-year PFS was 85%. These data suggest a potential role for the use of brentuximab vedotin as consolidation therapy after ASCT for children with relapsed/refractory HL.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".