Real-World Outcomes of Autologous and Allogeneic Hematopoietic Stem Cell Transplantation for Relapsed/Refractory Hodgkin Lymphoma in the Era of Novel Therapies: A Canadian Perspective
Bibliographic record
Abstract
Despite high cure rates with frontline therapy for Hodgkin lymphoma (HL), approximately 30% of patients will relapse or develop primary refractory disease (R/r). Autologous hematopoietic stem cell transplantation (autoHSCT) is the standard of care for R/r disease, and allogeneic HSCT (alloHSCT) is a curative option for patients in second relapse. Novel agents are being incorporated for the treatment of R/r HL, such that the optimal timing of transplantation is currently being challenged. In this rapidly evolving field, we sought to offer a Canadian perspective on the optreatment of R/r HL and demonstrate the role and effectiveness of both autoHSCT and alloHSCT for the treatment of R/r HL. This single-center retrospective study examined outcomes in 89 consecutive patients with R/r HL treated with autoHSCT between January 2007 and December 2019. A total of 17 patients underwent alloHSCT either as a tandem auto-allo approach or as salvage therapy. With a median follow-up of 5.0 years, the estimated 5-year PFS and OS for patients undergoing autoHSCT were 57.5% (95% confidence interval [CI], 45.2% to 68.0%) and 81.3% (95% CI, 70.0% to 88.8%), respectively. Corresponding values for patients who underwent alloHSCT were 76.5% (95% CI, 48.8% to 90.4%) and 82.4% (95% CI, 54.7% to 93.9%). Nonrelapse mortality at 0% at 100 days and 9.4% at 5 years post-autoHSCT and 0% and 5.9%, respectively, post-alloHSCT. The cumulative incidence of acute graft-versus-host disease (GVHD) at day +100 was 35.3% (95% CI, 17.7% to 62.3%), and that of chronic GVHD at 1 year was 23.5% (95% CI, 6.9% to 45.8%). Both autoHSCT and alloHSCT provide robust and prolonged disease control New agents should be used as a bridge to improve the curative potential of these definitive cellular therapies.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".